During snowy winter months, motorists may encounter hazardous roadway conditions. Particularly, this might occur when there are snow removal operations underway. This presentation describes these operations and addresses maintenance remedies to apply in post-snowstorm cleanup. The driving public expects clear streets and highways on a year-round basis, even during periods of heavy snowfall. Significant manpower and financial resources are expended annually for snow and ice control programs by state, county, and municipal governments. According to a Transportation Research Board's special report, state and local governments spend over $1 billion annually for snow and ice control. This presentation, primarily, is for state and district highway maintenance engineers responsible for managing and operating snow and ice control programs for state governments. However, the remedies described can be applied to any region of the USA. The project goal is to identify actions that can be implemented with existing manpower and equipment budgets. Snow and ice control programs typically are divided into two parts. One, programs for emergency snow removal to clear traffic lanes, which takes place during and immediately following a snowstorm, and two, programs for post-snowstorm cleanup, which takes place between snowstorms after emergency snow removal has been completed. Emergency snow removal is necessary for clearing or opening highway travel lanes. Hazardous conditions may be created or overlooked during the removal operation because of time constraints involved in emergency snow removal. Once a roadway network is reopened to traffic, maintenance crews perform post-snowstorm cleanup in preparation for subsequent storms. Cleanup includes removing snow that has accumulated adjacent to shoulders in order to make more room for additional snow. Cleanup also entails clearing snow from the high side of super elevated curves and ramps to prevent refreezing of melted snow. Cleanup means removing accumulated snow from bridge parapets and guardrails to prevent vehicle ramping accidents and also includes removing snow piled at intersections. Removing snow also from interchanges improves sight distance, thereby reducing the chance of severe collisions. Restoring roadside features to full operational effectiveness as soon as possible after a snowstorm is of the highest safety priority. State snow and ice control policies and guidelines address some, but not all, safety issues. State Highway Maintenance Engineers report that snow-related hazards during post-snowstorm cleanup operations are common and need to be remedied. This study identifies snow and ice removal hazards and it groups them according to their relative importance, most serious, serious, and important. A hazard ranking is based on the risk or the possibility of an accident occurring, the severity of an accident that could result, the ease of correcting the hazard and driver exposure to the hazard. All identified hazards can have serious adverse consequences to winter traffic safety. The four hazards ranked most serious should receive high-priority treatment in post-snowstorm cleanup operations. The most serious ranked hazards are snow accumulation on super elevated ramps and sharp curves, snow or ice accumulation at bridge parapets, railings, and curb areas, traffic vehicles colliding with snow removal vehicles or equipment, and snow left piled at intersections, interchanges, and curves where motorist vision is obstructed. Snow left to accumulate on the high side of curves can melt and re-freeze, creating sheets or patches of ice on the once-cleared road surface. This is called freezeback. It usually occurs when piled snow is melted by de-icing chemicals or solar heating. This allows moisture to run back across the previously cleared driving surface. This moisture then freezes in the cold of early evening, which is especially hazardous because it is unexpected and not seen by motorists. Super elevated curves, ramps, and bridge decks that have a flat cross-section especially are susceptible to freezeback. Snow stored on the high side of super elevated curves should be plowed or winged over the high side edge of the shoulder line and ditch. This helps prevent drainage of snow melt onto pavement. Widening the cleared area on curves alleviates the problem of reducing sight distance, resulting from large piles of snow left in the middle of the curve. On super elevated bridges and ramps, snow should be plowed to the low side edge of the shoulder. This is best done during off-peak periods of traffic, with plows working in tandem to move the snow across several lanes simultaneously. The second most serious ranked hazard is snow or ice accumulation at bridge parapets, rails, and curb areas. Many times during emergency snow removal on bridges, snow is plowed against parapets and rails. If this snow is not removed, it becomes hard packed. If piled and left at two-thirds the height of the bridge parapet or rail, it will create high potential for vehicle ramping accidents. The most critical areas on a bridge are the parapet, rail, or curb along the south side of an east-west oriented bridge. Because that side is always shaded, melting is delayed. Safety shaped bridge parapets, regular bridge parapets, and open pipe bridge rails are subject to vehicle ramping accidents, which can result in fatal injuries. Light snow can be pushed off short bridge decks with a plow, but plow speed must be reduced while working on overpasses to prevent throwing snow over parapets or through railings onto the roadway, railroad tracks, or buildings below. Plowing in tandem can be used to clear an entire bridge deck, including railings. Plowing in tandem is most safely accomplished under well-lit conditions, and when traffic is light. A moving barrier, such as a police vehicle with revolving lights, can be used to prevent motorists from passing the convoy of plows. After a snowstorm, when visibility again is relatively good, use warning lights to alert motorists. Remove snow and ice as completely as possible on bridge parapets, rails, or curbs. If the removal is incomplete, when temperatures rise to cause melting, moisture from remaining snow may run back across the pavement. With a temperature drop, a freeze-back condition is created. An extreme temperature increase can cause unsupported snow accumulation to collapse and spill onto the travel surface. Remove all snow accumulations either by blowing, hauling away, or pushing snow to the end of the bridge and overfill area. The potential for vehicles colliding with snow removal equipment is another most serious ranked hazard. Differences in operating speed between snow removal vehicles and traffic vehicles and poor visibility created by blowing snow creates the high potential for collisions. Many fatalities have been caused by rear-end collisions with plows. Head-on accidents have occurred when plows were operating near the centerline facing oncoming traffic. Keep in mind that vehicles return to their original operating speeds as soon as roadways are cleared and reopened. This speed shift adds to hazard potential at a site. Snow plow operators complain about motorists' driving habits during removal operations. Because motorists generally do not understand snow plowing operations, frequently they misjudge width, length, and speed of the plowing vehicle. Impatient motorists often speed, tailgate, or try to pass plows. Sometimes, while attempting to pass, motorists misjudge a plow's operating speed and hook their rear bumper onto the plow blade. To reduce potential collisions with snow removal vehicles, high-intensity lighting devices visible to other drivers both in front and to the rear, even through blowing snow, are recommended. Lights to indicate plow width are also recommended. For example, side clearance lights can be mounted at cab height and swivel into place for snow removal operations. The New York Throughway Authority has been using flashing arrow panels on its vehicles to alert motorists. All of its plows have been wired to enable lighted arrow panels to be interchanged between vehicles. The traveling public needs to be better informed about the actual difficulties there are in driving, handling, and maneuvering snow plows and other equipment. Public service announcements have been used for educating motorists on how to drive during winter conditions using the theme of give them room. The fourth most serious ranked hazard is obscured visibility due to snow piled at intersections, interchanges, and curves. Snow left piled near intersections can affect a motorist's ability to see oncoming vehicles at intersecting roads. These piles of snow also block an oncoming motorist's view of cars at intersections. Unsignalized four-lane intersections should be cleared first. In interchange on-ramp areas, piles of snow can hide merging vehicles and vehicles stopped at crossroad intersections. There is a location near the gore point of on-ramps where entering vehicles can be hidden by snow piles and cannot see mainline vehicles. High-speed accidents resulting in severe injuries can occur if these intersecting vehicles collide. The following five potential hazards are ranked as serious. Snow or ice on guardrails, improper alignment of snow plows, stalled or abandoned vehicles impeding snow removal operations, lack of snow storage areas at narrow medians, shoulders, and gores, and snow left on barriers. These hazards, although serious, should be treated after the higher-ranked most serious hazards are remedied. Unsafe conditions may result if snow or ice is left on guardrails. These include possible melting and refreezing, reduced effectiveness of the guardrail section due to piled snow, and snow drifting onto a clear road. Snow or ice accumulation along guardrails may result from drifting snow or plowing operations. This is more of a hazard on freeways than on other roadways because of the greater lengths of guardrail, traffic volumes, and vehicle speeds. To prevent long-term snow accumulation around guardrails, plowing out to the guardrails is recommended as soon as possible after a road is reopened to traffic. To prevent drifting and snow melt, clear snow from the shoulder inside of guardrails and near the start of guardrail sections. Guide stakes or delineators used at guardrail ends help plow operators locate guardrail lengths, even when they are completely covered with snow. Improper alignment of snow plows may cause traffic following the plowed path to drive off the traveled way. Improper alignment also can decrease usable lane width. This serious hazard arises from large snow plows being unable to negotiate the small radius curves of highway ramps. Also from plows wandering off the pavement area because roadway edges are not delineated. Or from inattention of the snow plow operator, who often works long hours until the roadway system is in acceptable operating condition. Guide stakes are used to delineate the edge of pavement and also to help plow operators maintain proper plow alignment. Within interchanges, guide stakes should be installed at 50-foot intervals. Guide stakes can be spaced farther apart between interchanges and on rural routes. To preclude the costs of an annual installation removal, some states leave them in place year-round. Stalled or abandoned vehicles impede timely emergency snow removal and cleanup operations. This is ranked a serious hazard. Severe snowstorms occurring at rush hour in urban areas create the worst problems because of the higher volumes of traffic using them. In rural areas, stalled vehicles can force highway agencies to close roads. Blowing snow particularly is an acute problem in areas where high winds cause drifting. Tractor-trailer combination trucks are susceptible to run-off-the-road accidents and present the biggest problem in clearing roadways because they are difficult to remove. To help prevent the problem of stalled vehicles during heavy snow, use public service announcements to warn people to stay home or to exit the road and sit out the storm. Use of commercial tow trucks for the removal of stranded vehicles should be part of a plan to support snow removal operations. Likewise, commercial snow removal vehicles may be used in a plan to supplement normal plowing operations. Lack of snow storage areas at narrow medians, shoulders, and gore areas is another serious hazard, especially when snowfall has been heavy or when storms occur in close succession. When piled snow melts, the run-off can re-freeze and cause localized icing. Piled snow also can obscure vision. It can even collapse onto the travel way during melting conditions. On multi-lane highways, to prevent snow accumulation in narrow medians where there is less than 20 feet of storage width or raised concrete median barriers are present, allow the snow in tandem from the left shoulder fully across the roadway to the right. Otherwise, snow will have to be loaded onto trucks and hauled away. Use wing plows to create extra storage area on shoulders. In gore areas, a motor grater or hydraulic front-end loader should be used to remove snow. In highway gore areas, excessive sanding and salting should not be done on bare pavement. They can act like ball bearings, causing vehicles to skid, to strike signs, and other appurtenances. The pavement may require sweeping. Snow and ice piled against roadside or median concrete safety-shaped barriers, especially in areas of frequent and heavy snowfall, is another serious hazard. As with bridge parapets and rails, snow piled at two-thirds the height of the barrier changes its shape and creates a vehicle-ramping hazard. The hazard is compounded in areas where vehicles can strike barriers at angles greater than 15 degrees, such as on the inside of curves and in wide medians. That side of the barrier facing the sun may be clear of snow, but the shaded north-facing side may contain packed snow. Normal plowing procedures dictate removing snow from the lower face of a safety-shaped barrier, but not removing packed snow that forms a ramp on the upper face. It is best to clear accumulated snow from barriers before it reaches two-thirds of the barrier height or hardens. In Brown County, Wisconsin, a heavy rubber blade attachment, as shown here, aids in clearing snow away from barrier faces. This rubber plow attachment was designed to scoop snow out away from safety-shaped barrier faces. Attachments can be made for either the left or right side of a snow plow blade. The solid rubber attachment is one-and-one-half inches thick heavy rubber belting material. The cost is about $250, and the device should last two or three years if used properly. Another device Brown County uses to remove snow from safety-shaped barriers is a metal attachment for snow blowers mounted on front-end loaders. Several important snow and ice accumulation hazards also are remedied during post-snowstorm cleanup operations. These important ranked hazards do not require immediate attention except where obvious problems exist. Some of these hazards are associated with drains, culverts and channels, snow or ice windrows, shallow cut areas, and at-grade railroad crossings. Other important hazards to be considered include snow accumulations at pavement obstructions, such as curbed and raised areas, snow or ice obscured highway signs, and snow and ice-covered impact attenuators. Accumulations of snow, ice, or debris at clogged drainage channels, culverts, and grade openings can be hazardous. But when snow melts on the pavement, pools of water can be created which also may freeze. Heavy rains occurring before a snowstorm can increase this hazard. Arrows painted where they will not be obscured on barriers, parapets, or pavement indicating presence of drains, aid in locating drains when they're covered by snow. For example, the New York Throughway Authority marks drain locations, even giving the distance from edge line to the drain. When clearing bridges, check drains to see that they are clear. Clogged drains may require thawing out and hand cleaning. Drainage problems at culverts are more common in areas with a deep frost depth, where ice remains in the culvert. In the spring, rain and melting snow can build up on the roadway and re-freeze, creating slippery spots. A steam generator can be used to melt the ice in this and other areas. Clear drainage channels have drifted or accumulated snow that have a history of snow and water backup. In anticipation of thawing weather, clear these areas with a hydraulic excavator or similar piece of equipment. A snow or ice windrow prevents water from running off the pavement, especially in hilly areas. Breakout openings in the windrow to permit drainage. Snow or ice windrows may build up at gore areas of highway on-ramps and at roadway intersections. These are important ranked hazards that present an unexpected obstacle to drivers. To mitigate a windrow hazard, adjust plow speed and moldboard angle. Plows operating singly should continue plowing until snow and windrows have been moved all the way to the right. If more than one plow is used, plowing activities should be coordinated so that the second plow is in tandem behind the first, on both the mainline and ramps. Snow blowing or drifting across a road can block travel lanes because there may be a lack of storage in cut sections. This is another important ranked hazard. This hazard is aggravated by successive snowstorms over a short period. Roads built perpendicular to the usual wind direction also have definite drifting problems. Remedies to blowing or drifting snow include installing snow fences, winging back, benching drifts, using a snowblower when a shallow cut is full, and loading and hauling away snow. A warning vehicle is needed to alert approaching motorists that roadside cleanup is in progress. Snow or ice pushed onto at-grade railroad crossings is another important ranked hazard. Once snow, ice, or aggregate gets into flanges or rails, it's difficult to remove and can cause derailments. To alleviate this hazard, empty the plow along the shoulder in advance of an at-grade railroad crossing. This avoids carrying snow or slush containing aggregates onto the tracks. Approaches to railroad crossings should be treated to prevent slippery conditions, but chemicals must not be spread on the track area itself. Plow operators must use extreme care when working around railroad tracks because the extra length of their vehicle and noise of operation can prevent their hearing and approaching train. Adjusting the angle of the plow so that it is not parallel to the angle of the railroad track will prevent the plow blade from dropping into track channels. Also, care should be taken to not plow snow onto railroad tracks from overhead bridges. Pavement obstructions, such as curbs, raised islands, rumble strips, delineators, buttons, joints, and manhole covers, especially at traffic-channelized areas, can prevent complete snow and ice removal. Remove snow from these obstructions before it has a chance to melt, spread on adjacent pavements, and re-freeze. In areas of heavy wet snowfall and high winds, snow-obscuring highway signs is another important ranked hazard. Snow thrown by plows also can adhere to signs. An unsafe condition exists when motorists cannot see a sign legend, especially those on warning and regulatory signs. Sign posts can be struck to shake snow loose. Fortunately, snow adhering to signs usually melts and slides off when the sun comes out. In heavy snow areas where drifting snow covers signs, dig back an area in front of the sign, especially for those in critical places. Still another important ranked hazard is snow or ice accumulation on impact attenuators. The attenuator may become jammed with snow or ice from emergency removal operations. An out-of-control vehicle can climb snow-covered attenuators and impact the hazard it was to be protected from. Impact attenuators operate well under most adverse winter conditions, when snow and ice accumulation does not hinder operation. To avoid cracking sandbarrel attenuators during snow cleanup, carefully remove snow with a plow or other equipment. When filling sandbarrels, mix 20 to 25 percent salt with the sand to prevent freezing. A special hazard category exists in mountainous regions. Where there are sustained steep roadway grades, there is high potential for runaway trucks. Truck escape ramps are designed to safely stop these vehicles. In the winter, however, a hazard develops if ramp approaches have not been adequately cleared of snow during emergency snow removal operations. Windrows of plowed snow left at entrances to escape ramps form hazardous barriers that can cause runaway trucks to become airborne, thereby increasing the severity of runaway truck accidents. Clearly define the approaches to truck escape ramps, so that runaway trucks have a wide enough clear entrance to the arrestor bed. The arrestor bed itself becomes less effective in slowing trucks if melted snow and ice are allowed to re-freeze. The arrestor bed should be cleared and its crusty surfaces broken up. A small tractor can be used to clear snow from the arrestor bed. The tractor can break up crusty bed surfaces with a rear-mounted fork attachment. A four-step procedure is recommended for determining priorities in post-snowstorm cleanup and may be made a part of any comprehensive snow removal plan. The four steps in this procedure are, one, obtain an inventory of existing safety features. Two, consider hazard categories. Three, determine remedies. And four, develop local cleanup priorities. The procedure recognizes varying conditions, geography, time of year, equipment available, and other factors specific to each state maintenance department. In implementing the noted four-step procedure, it is important to give support to the maintenance engineers. In determining remedies for most serious and serious hazard categories, the use of experience and engineering judgment should be exercised. More information concerning the hazard categories and remedies described in this presentation can be found in the Federal Highway Administration Technology Sharing Report, titled Safety Restoration During Snow Removal Guidelines.


We are here in New York Comic Con today to test people's knowledge about fake brands from popular culture. I'm Futureama! Oh no, no, no, no! Oh, Simpsons! Nope. Not gonna happen. Oh, that's you. That's Futureama. That's Slurm. This is gonna be great for your viewers. I feel like that's gotta be like, like, Jurassic Park. Jurassic Park? Toy Story? That's from Cars. Yeah! Yeah! See, I got one! All right! That is from Cars. Is that from Marvel? Tarantino. Kevin Smith. Fallout. Fudge. Oh, I got this one. Star Trek Keepsays 9. Silo? That's Luminance. She's from Sovereign. Resident Evil? Isn't that a Doctor Who reference? Wow, I am much worse than I thought I was gonna be at this. To be fair, we live in Barbie Land, so... Yes, we live in Barbie Land. We have none of those brands, actually.


What is going on everybody? Welcome back to my channel. For those of you who are new around here, my name is Michael, aka Dr. Cellini and I am a fifth year interventional radiology resident physician. On today's video, we are going to be talking about the much anticipated mouse that you've seen me talk about on prior videos. So many people have asked me to do a full review on this mouse and why I love it. So that is exactly what I'm going to do today. So let's go ahead and get into it. So it is sponsored by Board Vitals. Now you may remember on my previous videos that I am a huge fan of Board Vitals because they've helped me in more ways than one, including having the most exceptional radiology cubane for the American Board of Radiology Core exam. It literally is exactly like the examination and I can't recommend it enough. And that radiology question make is not the only thing exciting coming out of Board Vitals. Their step one question make is just as good. These questions are all written and reviewed by top specialists who have taken the exam and have experienced writing questions. Question content is mapped out closely to follow the USMLE step one content outline. With more than 2,750 exams on the USMLE step one Board Vitals Cubane, you'll get plenty of practice time before actually sitting for the exam. Also, you can now study for free on the go by downloading their new Board Vitals app available for free on Apple Store and Google Play and use my code DrCheleney to get 20% off anything on their website, including the USMLE step one Board Vitals question bank. All the links will be in the description below. Now let's get back to the video. Get into the much anticipated mouse review. So I'll first tell you about why I even use this mouse to begin with or how I came about finding a gaming mouse. So we had a guest grand round speaker come in and talk to us about all the newest and latest greatest technology and how it helps radiologists become more efficiently in their day to day practice.  And then I kind of forgot about it. And then one day when I was on night shifts on my diagnostic call session, I sat down at a station and somebody had a Logitech gaming mouse, which was similar to this one. And I sat down and has buttons on the sides and I sat down and started using it. First of all, I love the ergonomics. And then I also realized that the buttons on the side can be programmed. And it was making my life so much more efficient that I started doing research that night with my co-resident and both of us ended up buying a gaming mouse that night. So both of us ended up going with the UTEC Smart Venus fully programmable gaming mouse. One of the fellows in my department actually had this mouse and I tried it out and I liked it far better than the Logitech mouse. So I went with this one. So I'll tell you why I chose this one. But first, let me talk about all of the things that I love about this mouse and we'll try to break it down by a section here. We'll start with the design. We'll go into the specifications. Then we'll talk about why I love it for radiology. All of the cool features it has, the price of the mouse, who is the mouse good for, and the downsides of the mouse. First, let's get into the design. So first and foremost, the mouse has a nice weight to it.  It also has this nice resting plate on the thumb that makes it easy to kind of scroll and hit the buttons on the side. It's also weighted for stability and you can kind of change these weights as you see fit. The scroll wheel is nice and smooth. There's no lag behind it. It doesn't have one of those resistance free wheels that kind of just fast scrolls and keeps going until you stop it. The reason I don't like that is because I like the tactile click. It's a very subtle click, but I need that while I'm scrolling through a lot of images. If you get caught up in the free scrolling, sometimes you can just fly through a CT or MRI and you may miss stuff. Also the ease of getting to the buttons is pretty nice. I have pretty big hands and it doesn't take much of a reach or movement to get to these buttons. It fits nicely in the hand as well. So let's first discuss the elephant in the room. Why in 2020 did I go with a wired mouse when there are so many wireless options? The answer to that is I cannot have any lag while looking at images. Two years ago, I brought a wireless mouse into the reading room and plugged it in and as I was scrolling through images, it was laggy. Sometimes it would skip certain images and I literally took it out and threw it on the ground. I never use it again and I can't sometimes in a lot of the newer wireless mouses aren't as laggy, but something as serious as reading someone's image, you literally cannot have one little skip of lag anywhere. So I went with the wired mouse because I don't want to worry about lag whatsoever because it could be detrimental while reading an image on a patient. So you'll notice a lot of gamers also choose wired mouses as well. It's kind of the same thing. I mean it's not really that serious if they get shot or something while playing a multiplayer game, but it's along the same lines. All right. Next, let's get into the specifications of the mouse. So for starters, it is compatible with Windows 10, 8, 7, Vista and Windows XP. There are five savable memory profiles, which means I can plug this up and switch the profile so five different users can use this mouse and have different settings that are customized to them. So if five radiologists were using this same mouse, everybody has different kind of settings and they could sit down switch to their profile and everything would be stored in this mouse. There are 19 programmable buttons. We have 12 on the side. We have four of the DPI buttons up top. One, two, left and right click buttons and also a button right here as well. This mouse has 16,400 DPI laser sensor and I had to look that up because I have no idea what the heck that means besides it's very sensitive. So these buttons on the top actually adjust the settings of your DPI from low to very high. So DPI stands for dots per inch. It's basically how many pixels the mouse pointer will move when your mouse moves one inch. So for instance, 1600 DPI means that for every inch your mouse moves, your mouse pointer moves 1600 pixels. This mouse goes up to 16,400 which means every inch your pointer flies across the screen and moves 16,400 dots per inch or pixels. So we have pretty large monitors at work. So I actually adjust this. Each computer is kind of set up differently. So every time I move to a different computer, I can kind of adjust my DPI on the fly. That way I can kind of customize how fast I want to move across the screen and this also matters a lot when you're measuring small things. So if I'm reading a CT scan and trying to measure a six millimeter lift note, I don't want to have my DPI too high because it's going to be really hard for me to measure something really small because my pointer is going to want to fly across the screen rather than make a very subtle movement and measure. So I like being able to adjust on the fly like that. This mouse also features weight and balance tuning. It has eight five-bram weights to personalize your weight and center of balance to kind of customize it to your feel. I like it pretty heavy, feels nice on the hand and I can just make subtle movements and they are very precise. It also has these smooth Teflon foot pads that help you kind of glide across the surface effortlessly without too much friction. Furthermore, it has a braided six foot cord with a gold plated USB plug. So this is actually pretty nice, pretty sturdy as well. So now let's talk about why I love it for radiology. So like I said before, all of these buttons are completely customizable. You can literally program them to do anything and I'll show you what I have mine programmed to. So for instance, the one is programmed to the number one, which changes to soft tissue window on CT. The number two button is the lung window. Number three button is the bone window because three looks like a B. That's how I remember it. That's how it's been for five years now. Four is the liver window and I don't have anything programmed for five or six.  So I actually programmed the number 10 button as the number one, so it's soft tissue. The number 11 button is also the lung window, just like number two. And number three is the bone window or number 12. So depending on which ridge my thumb is on is how I determine which button I'm pressing up top. I just have the DPI buttons programmed to the how fast the mouse moves. Some people programmed these to bring up Google Chrome or a search engine. I programmed this top third button as the zoom button and that's pretty much all I have. And that does seem pretty basic, but I'm telling you there's nothing more efficient than scrolling through an image and being able to change windows as you go. So before using this mouse, I used to have to hit the numeric keys on my keyboard to change the windows. So I would have my dictaphone in my left hand and every time I wanted to change a window to say lung window, instead of pressing two on the side button, I would have to put my dictaphone down, hit the number two button, pick it up again, start dictating, put the dictaphone down, go back to soft tissue window, hitting number one, start dictating again, and you can see how that is so inefficient. So once I got a hold of this guy, kind of changed my life. So my favorite feature of this mouse besides how efficient it can be and besides how much you can program it to literally do any function you want is the color scheme on it. So it's actually backlit with LED lights, which you can customize to over 16 million RGB colors. I have this kind of pink and purple blue tone to it. I used to change it depending on my mood. If I was really tired, I would put it all red. I don't know. So now let's talk about the price. It looks pretty expensive. This must be a seriously expensive mouse for how much it does, but actually it's only $32.99 on Amazon, which is why I pulled the trigger the night after looking into it. I mean, you don't really have much to lose, but this was worth every penny in my opinion. And by the way, I am not sponsored by this company. I just really love this product and feel like I should share it with you all because it can make your life so much easier if you use this mouse and use it appropriately while reading studies and radiology. So all in all, for everything this mouse is capable of, $32.99 really isn't that bad. So now let's talk about the downsides or the negatives of this mouse and honestly the only one I could find is that it's not compatible with Mac or Apple products. It sort of is, but the driver that comes with the mouse in the box can only be loaded on PCs. So luckily all my computers at work are run by PC. So I can load the driver on there and you know, change the colors and adjust all the settings on there. So the downside is I only have a MacBook at home, which means when I plug it into my laptop, it does have all the colors and saves the settings, but I can't change the settings on my home computer. So it's not that big of a deal because I probably won't change the settings now that I have customized it to my liking, but if you don't have a PC to plug it into, it's probably not that good. Alright, so that concludes this video. I hope you all enjoyed it and I hope you all liked my review on this mouse. Let me know in the comments below if you are going to get this mouse. I will also leave a link to this mouse in the description below, so you guys can go get it if you want it. As always, make sure you hit that like and subscribe button and follow me on Instagram if you don't already. Turn on those post notifications so you are notified when I post a new video, which is usually about once or twice every week. Leave a comment below. If I liked it, I'll respond to it. Otherwise, I'll see you all on the next video.


Hi everybody it's Sandy and today I'm going to test out the fluorescence of the fluorescent Copic colors. I'm going to tell you about a new class coming and I'm going to go over a little bit about the new Copic reinkers that are finally making their way into stores. Let's begin first with the reinkers because that is going to be of the most interest probably in this video. I bought a couple of them. They are called Copic ink instead of Copic various ink. They're not called refills or reinkers but they have 12 ounces in them. The old ones had 25 so they are about half the size of the old bottles which means they're going to be easier to store because they're tinier but these are inks in them for refilling. They're not pens. They look like a pen. They're just pen shaped and you take the plastic off and unscrew the lid. They are twist tops and you can see the ink inside. It's a little easier to see on camera with this lighter color and I like that you can see that because it's going to be easier to tell what you need to get a new one of. The nib on the end is much longer than the old one which is going to make me less of a mess when I am filling my pens. Super excited about that and I'm super excited that Ellen Hudson finally has some of them in stock. She's going to be waiting for a long time as is everyone else to be fully restocked. It's taken them a while to get them out so get what you need as you find them. For the card I counted how many octopi I had and decided to make an octopi party card. I did them all in a long tall card this time because I wanted to have a deep sea picture. I've attached it just with a little adhesive onto my craft assistant so I can do my airbrush spraying with the Copic airbrush gun put the chisel nib in and start spraying. Now some of my pens work better than others. If you use a brand new chisel nib and a nice full marker it's much easier to do. I end up fighting them and wiggling the nibs and that sort of thing to get ink to come out half the time because I don't feel like re-inking or I don't feel like changing the nib because a lot of my nibs are the original ones that I've had forever in there and you can't color with them. They're so dry and crusty so they don't airbrush well when they're all dry and crusty so if you're having trouble that might be why. With this card I debated whether or not I was going to try masking them all out or just cut out the octopi afterward from a different piece of paper color them and glue them on and as I was doing this I thought let me see if I can create some highlights on these without having to deal with all the fussy cutting it was going to take to glue these octopi on here the one at the bottom from Trinity is super detailed the the two middle ones are long fond and they have lots of arms this little guy from Ellen Hudson with the big eyes I love him for his big eyes he's got lots of little twisty arms and things it would be a nightmare to cut out as well as the little one from Waffle Flower up on the top with his hat he's so cute and his little pirate hat so I decided I was going to try to keep away from spraying on them and before doing all the coloring on another piece of paper I would see what happens if I color into this background I tried eliminating some of it by just going around their heads so that I could have less color to deal with with coping markers the marker itself pushes out color from where it is if you use a lighter color on top of the darker one so here I'm using one of the fluorescent blues to make bubbles and you can see it does lighten that color I can use it to color on the image as well even though there's blue ink there I can use other colors to push that blue ink out of the way you could do that with a just the clear marker the zero and push it out of the way with that and then color into it but then you have a lot of moisture in the paper so I just went right in with the colors themselves specifically the fluorescence I thought it would be really cool to see how fluorescent the fluorescence really are because now if you remember a video I did a while back I bought a black light flashlight for my black light ink so I have some glow in the dark ink and I wanted to try to see if I could use that with these so I'm coloring first just to see what happens using some other colors along with the fluorescence because I wasn't getting any depth of color with these they're kind of bright and garish sort of colors for the most part but I decided to try them all out on the different octopi and see what happens now this orange on top of the blue was a nightmare it came up really really bad it did not want to push the color so if you're going to try this technique of pushing it out from airbrush color then I would try a little swatch on a piece of paper first and see which colors are going to work and which colors are not it could have been because that marker needed to be re-inked it wasn't pushing enough out I don't really know but I decided that one was just going to start getting darker and then as they're getting deeper into the ocean they're getting darker anyway so it kind of worked so I went in with a dark marker to do that all the colors that I'm using on this by the way are in a picture on my blog if you want to see what they all were because I don't remember while I'm sitting here editing the video I'm trying to get this thing out for you so I kind of kept going back and forth on this one to add more of the fluorescent orange along with the darker color and then I decided to just let it go for a bit it was getting really soppy wet and I would go deal with it later once I saw how it dried the bottom one I'm going to use the fluorescent violet and the fluorescent blue and see how the two of them work and those were nicely re-inked so they had a lot more color that they could push out in order to create some highlights but I like the way that this whole thing was starting to develop with lighter colors at the top and darker at the bottom because it also disguised the fact that the stamp at the bottom here from trinity has thinner lines than all the rest of the stamps so it looks a little funky if you have them all in one picture if one has thin lines one has thick lines etc this hides that to some extent because really I'm just looking now at the highlights and letting all the dark colors fall into that blue the other thing about coloring underwater things is that unless there's a light shined onto something when they're filming it there's no color once you get down to the bottom of the ocean you might get a little filtering of light but you're just getting grays and blues if that so this kind of pushing of color in here is a little unrealistic but then again an octopi party with one of them showing up in a pirate that is probably also not particularly realistic so there's that as well so once I got all of these guys colored I added a little bit of texture down there with a couple different colors into the sand at the bottom of the whole panel and then I took all the different colors to add bubbles to it I thought that would be a really good way to see which ones when I shine the black light on it which ones show up which ones look brighter than other ones and then I also went in with a Posca pen which has nice white brightness to it and makes big dots and then my little signal pen my little ball signal so there are just bubbles bubbles bubbles go lower both dots in circles to make this a whole lot of fun so here is my black light it's a kind of purple light that you can get I think these are like 10 bucks or I think it was about 10 bucks I'm trying to remember about that on Amazon and you can see here that some of those colors do light up in the black light which is kind of cool I had them also swatched on a piece of white and you can't tell really here but there's only four colors that really show much and it's those three right there the yellow yellow green yellow orange and then a little bit of that fluorescent blue on the bottom octopi show up but it was kind of fun you can also tell by just shining the light on the caps which ones are really fluorescent and which ones they just call fluorescent and they're not these are also not glowing the dark they're not going to absorb light and then reflect it back when we turn lights off they need a black light so I'm not really sure the purpose behind this you'd have to send a black light flashlight to whoever you sent the card to for them to know that it's fluorescent so there you go now quick peek at the class that's coming up it's called Copic art journaling which I did years and years and years ago I finally got around to doing a class on it I'm going to show you not only how to do the techniques on the lesson spreads but on how to do something with the pages in between where the color leads through so I'll give you some ideas on that so you're not ending up wasting pages in between there's somewhere you can add other panels in it and some you can just do some coloring things to make it work lots of great techniques that you could also use for cards eventually so click the link in the doobly-doo to find out more about the class it's probably going to launch in a week or so from right now and that is it have I shoved enough into one video it still hasn't reached 10 minutes and I think I got a lot in there so thank you so much click the like button if you enjoyed this if you got something out of it and I will see you again soon go get yourself some reinkers and I'll see you later


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Good afternoon. I'm Jennifer Schenker, Editor-in-Chief of The Innovator. Welcome to the Growth Summit session on Future of Work, Science and Technology. Global labor markets are now facing a challenging combination of economic and geopolitical volatility combined with growing social and environmental pressures. According to the Forum's Latest Future of Jobs report 2023, 50% of workers will need re-skilling by 2025 and skill gaps in the local labor market remain one of the key barriers to technology adoption by businesses. While ensuring workers have the skills for the jobs of tomorrow is a key imperative, several sectors including AI and cybersecurity report difficulty in attracting and retaining the tech talent necessary for the world of work today. What's the size of the challenge and how can government and business come together to scale the necessary up-skilling and re-skilling investments? The objective of today's session is to understand the size of the problem, explore the role of up-skilling and re-skilling in providing workers with the skills of the future of work in science and technology, amplify existing workforce initiatives that can support in attracting and retaining science and technology talent, and identify effective public-private strategies that can close the digital skills gap and ensure workers have the skills they need for the jobs of today and of tomorrow. So with that, I'd like to introduce our panel. Next to me is Minister Azir Al-Yuh, Minister of Information Society and Administration of the Republic of North Macedonia. Welcome. Thank you very much. Lisa Mello, Vice-President of Learning and Performance for the Royal Bank of Canada and Oikir Izik, Professor of Digital Strategy and Cybersecurity at the IMD Business School in Switzerland. So let's kick off with you, Minister. What in your view is the global science and technology skills gap and what impact is it having on organizations today? Thank you very much for having me, Jennifer. So as you mentioned, skills gap really remind key barriers in industry and in society, but especially in local labor market, as you mentioned, and especially in small countries with limited resources related to infrastructure, technologies, labs, education, and so on. So today we are facing, it's a global problem. We are facing such gap and this is the reason why you hear daily about digital transformation, digital strategies, ICT strategies, cybersecurity strategies, and we are aware that we are facing this kind of, let's say, gaps and problems and it will be bigger and bigger every day because we invest a lot and industry invest a lot in new technologies, new approach of the problems from the other side, universities and educations are in, let's say, different gaps without not proper infrastructure, labs, know how they do a lot, let's say, research, theoretical research, not applied research, they don't have enough, let's say, collaboration and cooperation between industries and research and education. So we have to work in this way in order to be aware and to reduce the gap between what we train and what kind of approach we have in this way and what industry needs and what kind of approach they have in daily life. So what we do in North Macedonia, we have working group to create, to establish agency for digitalization. It will be umbrella in our country in order to deal with such kind of problems, gaps and to give solutions. So it will have four pillars. The first one, it will be for critical infrastructure and ICT infrastructure around the country. The second one, it will be academia trainings in order to reskill and upscale the public sector but also working together with industry. So the workers there, the third one will be application and development for public sector and the country and the last one for cybersecurity. So we think that with such kind of agency and approach, it will be much more easier for us as a small country with limited resources to reduce the gap. Okay, thank you. Let me turn to you now and ask from your perspective, from a global perspective, certainly government has a role to play here but so does private industry. What do we need to change in order to do a better job training, recruiting, retaining the right talent? Thank you, Jennifer. It's a pleasure to be here. When I talk to our participants, for instance, who attend digital programs at AMD, we do still see that there's a huge appetite in digital transformation still. Interest in AI, interest in cybersecurity, but we still do hear about digital transformation initiatives failing or going too slow, not achieving the results they want to achieve. So there's still this wanting to do versus actually doing gap there and they do talk about the need for more data scientists, more cybersecurity analysts in organizations, right? But I think I do observe another gap, right? The gap of the knowledge in non-digital executives and their understanding around what can I really expect from this technology? What is the business objective that I'm trying to achieve with this new project around, for instance, AI? So this is another thing that I think organizations can also tackle. You're asking what can they do, right? So there are different phases to the problem. There's the phase around, like the minister was talking about, the pool of talent. How can we grow the pool of talent? But there's also the challenge of hiring processes, right? What are we doing during that process? And when the talent is in the organization, how am I actually retaining the process? Just one small example. Several chief information security officers that I'm talking to, only half jokingly saying that half of my time goes to hiring, interviewing candidates, training them, only to lose them to my competitor after six months because the market is the employees market, right? And there are a lot of opportunities for them. And then I start the cycle again. So this cycle of hiring, interviewing and training cycles, of course counterproductive also for executives. So investing in self-development or learning and development initiatives in organizations and maybe training on the job is something else they have also started thinking about. Okay, Lisa, let me turn to you and your experience in the bank. So we know that the pool of talent isn't big enough. Every company is competing for AI talent, for cybersecurity talent. During this growth summit, there's been discussion about maybe we need to move the emphasis away from just looking at people's education and job experience to their aptitude and mapping skills from one area to the next. What's your point of view on that? Yeah, thank you for having me, Jennifer. And I agree we are not going to recruit our way out of this issue. And training and reskilling in the flow of work truly is a lever, I think, that we can all pull. You know, we've been at reskilling and upskilling for some time. However, what's changed is the urgency and the pace of change. So previously, we were identifying employees whose jobs were disrupted as a result of automation. But now we know there's so much more disrupting jobs than automation. So creating pathways that are curated, that are self-directed, self-paced, but with support, you know, so having access to learning circles and having access to coaches.  You know, degrees are sure they're necessary. However, you know, my degree, how many of us are actually in fields with the degrees that we have. You know, it changes so often. So how can we leverage micro-credentialing and stacking it up so it has almost an equivalency to a degree and working with our education partners and governments to really facilitate that? Do we need to rethink HR and the way we hire? So that's one question I will throw out to all of you. And the second part of that is there is now a growing adoption of AI tools by HR departments and they're being used to filter candidates. Are we not in danger of adding more bias into the process? How do we ensure that we are, you know, widening our view on, you know, who's good at what and who should be with a little bit more training could become the cybersecurity expert or do something completely different in the organization? Yeah, I would agree. Skills-based hiring, you know, versus hiring for jobs is a way to get at that and rethinking jobs. So, you know, can we think about re-skilling and skill-based hiring where, you know, the work is clustered and it creates internal mobility, you know, and it's not a job. That takes a lot of organizational effort and commitment.  And, you know, there are assessments before and after to help with the bias as well. I may think I have certain skills. My manager may think I have different skills. Where do we agree and how do we fill the gap, you know, and plan that gap out? So definitely, you know, mining more data of the skills that we have, the gaps that exist and how we fill them, and using tools, you know, to help with that. So maybe we can talk about what's working on the ground and what could be implemented to help move things along. I wanted to go back to a point that you made about retaining talent. Because if companies are having to retrain for the same positions, it's a waste of time for everyone. What do we need to do once we get the right people in the door, or we identify them and we help them reskill to keep them in place? Do you have a point of view on that? I think that definitely there is a way to bridge the situation. Sorry. So maybe the industry and then universities and formal and non-formal education needs to be closer to working and create curriculum study programs that are related to the needs, really needs and technologies that industry needs. So this is the only way. How can we boost the process and be in line? Did you want to add to that? Yes, I think adding to what Lisa was saying, micro-credentials or the concept of lifelong learning in organizations. It has an impact on the culture, the way we approach things. So maybe tying this to the role of learning and development and HR. So the need to realize that maybe indeed when you talk about technology-related skillset, we're talking about a very dynamic field that requires constant revision of capability, skills and expertise. So bringing that into the conversation means a much bigger responsibility on the learning and development teams in organizations. I think we also need to change the language a little bit. Just specifically talking about cybersecurity, right? Still looking into this team in the organizations, you know, the old joke around them being in the dungeon doing their thing. It's still, maybe not as much, but still kind of there. So the language around, for instance, cybersecurity that really associates it with a very fringe side of IT, it deals with this and that, as if it's distant from business, should be demolished, right? It is core element. We are going through digital transformation. Cybersecurity is a core capability, not as nice to have. So the way we frame this, the way we talk about these things in the organization impacts the way we approach it as well. I think so that also needs to change and be part of the culture and become part of everyday conversation in the organizations. Yes, I mean, in terms of, and also widening the pool, I have heard people say that a good cybersecurity expert, you know, needs to have emotional intelligence. And this is something, if that is emphasized, could perhaps attract more women to go into the sector rather than, you know, the boys in the dungeon. So I think that, you know, in terms of workplace diversity as well. And maybe, you know, this is true for government as well as business and at the educational level, we need to think about work in a completely different way and start with the premise that no one is going to have a job for life. No one is going to have the same profession for life. And that this is an exciting opportunity to continuously reinvent yourself. And I think that if we start in schools and then the workplace also tells you, we're not mapping your path for you for the next 40 years. We can't. It's going to evolve and you're going to have to evolve with it. That, you know, people will have maybe an easier time to adapt to change. But this is the reason why we need to create infrastructure for them in order to train and to have opportunities to switch from one to another, maybe work. And this is the reason why we have to work together with the industry to create hubs, accelerators, tech parks and so on. Are there some public-private initiatives you could point to that, you know, you think are working well or are there some you'd like to see? I'm aware of a few in Europe when it comes to not necessarily skill development, but for instance cyber security best practice sharing platforms, right? Or threat intelligence sharing platforms, which we also talk about that we need more of, right? Still, this is not a topic that is discussed very widely. So to foster that discussion, we need to bring together either the players in the same industry or even across industries with maybe the guidance of government to say let's discuss the common threats that we have. So we actually learn from each other. A similar approach can be applied here as well, I think. So we do need a systemic change. So industry opportunities to foster hands-on learning, right? Or internships for younger population, but also a revision of the curriculum, right? What are high school students already learning about this? Are we teaching the right topics in universities? And questions around that also would require multi-stakeholder meetings, I guess. I would add that internships pick up on that. You know, we offer at RBC many different types of internships. One that we're quite proud of is where you have university graduates who just are not able to get a job because they don't have the experience. So we bring them in and we give them experience in a client-facing type of setting, in a knowledge work type of setting, and then in a not-for-profit. So we partner with charities so that the individual gets not only corporate, but also not-for-profit. And we don't keep them, right? Then they go, and most of them are able to secure employment. So more of that internship, more work-integrated learning, as you were saying. You know, our employees want it. It really is a differentiator if you're able to offer that. Okay, more of that. Well, with that, I'd like to turn to our studio audience and ask who has a question. So thank you for your insights. My question was around, you know, skills that we don't measure. So things like curiosity, risk tolerance, drive, which often drive career outcomes much more than whether someone understands AI or cyber security. And I wonder what we can do as companies to measure those attributes better and then think about how we upskill those attributes that would really drive lifelong learning from the core versus a particular subject matter. Thank you. And can you just identify yourself, please? My name's Brajal. I run a startup call on loop. Thank you. Once to answer. I'll start. Thank you for the question. You know, I think assessment is important and a self-assessment, but also an assessment, 360, you know, type of assessment. And not just for, you know, hypotalent or, you know, executive talent, really democratizing earlier in your career. So you're able to build those skills if there is a gap. And we have so many tools available to us now, digital tools so it's scalable. And I agree with you, you know, you can't be, you know, a full, let's say, people manager, for example, if you've got terrific technical skills, but not great people management skills. So we, you know, we need to bridge that gap with assessment but also action plan, you know, to fill those gaps. Are you finding tech tools that can help you to do that? We're starting to and, you know, to really be able to mine the skills that we already have, right? In our company. Never mind what we need to build. You know, we've got employees for whatever reason, newcomers to Canada that are not credentialed. Yet they have a really good foundation. So having the technology to be able to go in and find those employees and proactively identify opportunities and pathways for them. You know, we're starting to use that more and more. And what about the learning tools for reskilling? How do you integrate that into the everyday work life? More micro learning.  It's micro learning. It's while you're in the flow. It's five minutes. It's, you know, questions. It's how confident are you in that answer? So it really does develop a personalized pathway, but it's scalable. And that's more of what we need. I can also add to that we are also combining the changing demand we hear from organizations when we are designing learning journeys, for instance, from organizations. It is very far away from indeed sitting in the classroom all day. We do observe the need for addressing different types of learning styles of individuals. Some prefer really, prefer self-paced learning. Some do want to be sitting in the classroom. Some really enjoy the Zoom environment, whatever you have, right? So there are different learning styles that we're trying to cater. And technology allows us to use a multitude of different things to address these different expectations. So clearly the tech tools are helping, but I want to go back to the Future of Jobs report. If we need to upscale half of the workforce in the next two years, are we moving fast enough? No. No. No. We have work to do. Yeah. And you need momentum and you need real commitment in the industry, not just in your company. Do we have any other questions from the audience? Can you please identify yourself? This is Jorge Cicilia, BVVA. I want to pick on this issue of data mining, because you can imagine that workers have a subset of skills that companies or governments demand a subset of skills. If you had all that information in that digital world and you could follow which skills are people getting that allow them to get a better job, and you have this information across society, I know it's difficult due to lots of issues, including data privacy, but should you have that information, maybe it would be easier for newcomers to know the type of skills they need to learn in order to get a better job. Can we do it better using that? Who wants to take that? Absolutely. Just one very quick comment. I think that's very much related to what you were saying, Jennifer, on, for instance, the role of emotional intelligence in cybersecurity. Communication skills, that is something we would often traditionally not attribute to contributing to success, right? But we now know I have met very interesting individuals who have background in social sciences, who have a background in philosophy, who are very, or psychologists, who are very clear and skilled in emotional intelligence, very good at talking about social engineering, very good at catching social engineering initiatives. So great contributors to cybersecurity awareness trainings, for instance, in organizations. So I absolutely agree with you that this is the separation from what you see on someone's diploma or certification versus what you gain about an individual when you observe them on the job and say, well, this actual particular capability could help you contribute to that side of business, right? So more than the diploma, more than the certification, but also aptitude, interest and other capabilities that we wouldn't expect to contribute, could contribute. I would add curiosity is important. Creativity in tech jobs as well. It's not only about critical thinking, which of course is important. So getting that whole employee is where we'll see the benefit. Would you like to add? Just to add, yeah, in this direction. This is, yeah, curiosity is very important, but in order to see in yourself that, okay, I will be good enough in this or that technology. You have to have, let's say, a common environment drinking coffee with different industries, academia. So, yeah, this is very important for strategies in order to create such kind of open space and bring together government, industry and academia and people interesting in different, let's say, fields in order to have change, to change idea, to have opportunity to talk together and to see the, let's say, opportunity to work and so on. This is very important. Great point. I mean, the exposure to the possibilities can be very inspiring. So as we get close to the end of our session, I'd like to ask each one of the panelists if there is one thing that you would do to help close the science and technology skills gap, what would it be? And I'll start with you. Okay. The two things pop in mind, one I already shared, the language, the changing the language. I guess I would go for more girls in STEM as the second element. This is still a pain point and we still also talk about diversity and inclusion and having more women in technology in general. But in cybersecurity, let me be even more specific. This is an acute issue, really, that we need to foster because we know that it does make a difference. It does bring different perspectives, different capabilities and skill sets on the table. And that starts as early as elementary school. So more girls in STEM. That's what I would do. Great. Lisa? Yeah, I would agree. And also getting proactive rather than chasing and being reactive. So for example, this time last year, we were desperate for employees with Python skills. Right? Now it's integration. It's architecture. So instead of chasing it, when it's behind, it's getting proactive about understanding what the coming needs are. Thank you. Minister, you have the last word. More communication, information, infrastructure, commonplace, yeah. Okay. Well, with that, I'd like to thank our panelists and thank our studio and online audience. Thank you very much. Thank you. Thank you.


Thank you. So We're gonna spend the next 30 minutes to talk about our use case which is experiment tracking hyper parameter tuning And some infrastructure we have to build internally solve some of these problems So first off just want to give you a little overview of what we're gonna be talking about today We're gonna spend the next 30 minutes just to go over high level. What is experiment tracking? what is hyper parameter tuning and Some of the challenges when building machine learning models We'll give some examples of unsupervised supervised machine learning and we'll show you a demo And yeah, let's get into it So when you're building machine learning Systems right you're building maybe machine learning models and you know, there are some challenges there You might use different algorithms to solve your problem You might want to use different hyper parameters to get the best results Sometimes experiments are hard to reproduce so if you deploy something a model into production You want to have kind of metadata of what hyper parameters were used what? All all these details what data set maybe the git hash if it was stored in git So you can kind of know exactly what code causes problem And then you know when you're training models there might be you know latency and performance Like for example when you're doing grid search it might be you know time-consuming to search all these different hyper parameters So Typical, you know software engineering Involves what like you write your code You test your code you deploy your code Maybe you do some integration tests and some CI stuff you monitor your code Pretty simple, but then But then like to be successful at software engineering and then be successful in building machine learning systems They have different goals for example software engineering your goal is to meet a functional specification with machine learning your goal is to optimize the metric for example accuracy of your prediction and Quality quality is measured maybe in traditional software engineering in your code And machine learning quality is measured with the code the data and tuning it And you're gonna be regularly you're gonna need regular updated data to keep on continuously training your model And then the process continues, you know, you're gonna be constantly experimenting with maybe different libraries and different models and Trying to productionize it and we'll dig into more details on What all this stuff means? so What are hyper parameters, right? So when you when you're creating a machine learning model You'll you'll be presented with like different ways to define your model architecture At first you don't know what the optimal architecture is You sometimes like is finding the right hyper parameters kind of like a Black magic, right? Like you don't know exactly how many How many clusters should you have in maybe k-means for example, right? You might have to try different Numbers of clusters to find that the optimal one We'll show you more details on how to do that, but just remember this one thing parameters That which you define in your model architecture are hyper parameters and the process of searching For the ideal model architecture is referred to as hyper parameter tuning Hem is gonna tell us some some examples of some hyper parameters that and give us more deeper details Yeah, um, so typically hyper parameters are numerical in nature So I have just given a few of them that we commonly sort of use in a machine learning model that you're trying to train So one such example is the number of epochs and the number of epochs actually go hand-in-hand with Another hyper parameter, which is the learning rate. So when you have a machine learning model You obviously have a huge amount of data so this is what we call as the training data that you need to feed to your model and With a lot of memory constraints, you cannot feed the entire Training data set at once so you would need to split these up into smaller Sizes which we call as batches So and what you do is you basically feed each batch one at a time to your model for it to train So a complete So when every batch is basically fed into the model at least once is what we say as one Complete epoch, right? So it's basically equivalent to when the model each of the training data set is completely fed into your model is what comprises of one epoch and the learning rates are basically a Small value which ranges typically between zero and one We commonly use this in a deeper neural network Models so these learning rates basically allow you to control the amount of weights that you can assign for your input data So there is really no fixed learning rate. Sometimes a high learning rate can be Really bad for your model because in the end you're trying to reduce the loss of your model as well So you're trying to see where is that optimal learning rate for which you're able to reduce The loss that the model is facing But also you're able to pass more number of epochs because obviously just having one epoch to train your data set is not Enough you need to constantly keep training your data So increasing the epochs, but at the same time reducing the loss function is what we are looking at Which are considered as two important hyperparameters and Then again you have in case of neural networks We know that a neural network has a single input layer and then you have a single output layer But the intermediate layer is called what we call as the hidden layers which comprises of different hidden units So what happens in a neural network is we're trying to create the input signal into a form of the output signal Which is typically like a zero or a one and what the hidden layers are doing inside is how do we sort of fine-tune the signal to get that output that we want again you have a bunch of Mathematical signal functions that you apply which we call as activation functions So these are basically when you add weights to each data points You're summing them up you're feeding it to your hidden layer and inside that hidden layer is where you can sort of play around with different Activation functions that you can apply to each of them so that you can fine-tune your input to the required output that you want So these are all again user defined parameters Which is why we call them as hyperparameters and you sort of do a trial and error each time that you're training your model So now that we know what exactly are hyperparameters, why do we need them right? So we need them because they directly control the behavior of your training algorithm in any machine learning Performance that you're trying to do it has a huge impact on the performance of this model So a good choice of hyperparameters really can make your algorithm stand out so Machine learning lifecycle you're getting raw data sometimes not clean data. You might have to clean your data And then you'd run your data through some training In that training box over there. That's where you'll be doing some maybe some Some feature engineering maybe some hyperparameter tuning and then you'll be deploying your model Maybe it could be a physical file or it could be a function and the process continues as you get new data keep on training Deploying and and also you want to monitor the live system of how well and how accurate the results are when you're running in a production environment and When you're deciding you want to decide either to do unsupervised or supervised machine learning unsupervised means like It's unlabeled data and supervised means that you have some labels to your data We'll talk about some examples of An example of each of these In a net next slide. So for example, there is k-means K-means is an unsupervised machine learning technique The way k-means works is Basically you partition your You partition and data points into k clusters here K is a numeric number Suppose k Basically and so similar data points are grouped into under one cluster say k is three and you have ten data points What k-means does is it it takes the features of each of these ten data points and assigns each point to Either cluster one two or three and the data points which are similar are grouped together in under one cluster And Hem is going to tell us more about that Yeah, so as Zach mentioned here. We're basically Plugging in different values of K. So that's an example of a hyper parameter for this k-means Model because you don't have a fixed number of clusters. So it really depends on how many clusters you want to sort of Group your randomly distributed data points. So this is where ML flow comes into the picture And we'll be talking more about ML flow after this So ML flow is the tool that we sort of think is pretty useful for these hyper parameter Tuning aspects because you can keep track of the different values of K that you are playing around with and how that helped improve the performance of each of your models respectively So now that we know what exactly is k-means and a little bit about the differences between supervised and unsupervised machine learning Where exactly can we apply k-means, right? So Typically k-means is useful when your data is numeric in nature When it's preferably that it should have a small number of dimensions But of course, that's not always the case and then you'll have to do further dimensionality reduction And also it's it's it's good when the data is also sort of having a continuous Nature to it. So that's where we would find k-means to be a suitable use case when you are trying to group things Which are similar together So now that we know What exactly is k-means and sort of where it can be applied? How many of you think you would have used k-means somewhere or have an idea of where k-means might be? Or that you've used something that uses the k-means algorithm. Don't always Okay What what did you use that use k-means So Like, um, yeah, I'm just gonna give us More details on examples of systems or apps or something that you've used that rely on So I'm sure all of us would have used uber or any of the other right sharing applications, right? So you have uber you've lived so all of these do have some kind of k-means algorithm behind it because When you're booking for an uber right you're usually paired up with some driver And if you're booking like an uber pool you have other passengers in the car as well So that's sort of how they use k-means to identify based on your location What are the nearest possible drivers that we can allocate to you to pick up and also they use it in their back-end where they do a More statistical analysis of rather than just using it in real time They actually use it for their purpose of identifying which areas or which locations had the most demand for Ubers at this particular time. What were the number of customers that got in and things like that where they have used? k-means Another use case is like an e-commerce or online Shopping experience again, they they do have a lot of integration of some kind of k-means in them Where they're trying to predict how they do the delivery estimation Allocating nearest truck drivers and things like that is where k-means is used Another one is It's sort of more complicated use case of it But they do when you are do a lot of research about a particular location or area You're trying to figure out if it's safe or not. So you have these third-party Websites which kind of give you a statistics about the crime rates in these localities So they do a lot of training on pre Historical data over the past years and they sort of give you that insights into which areas are probably safe or not And then another use case would be Netflix, right? So when you are streaming on Netflix, they have a bunch of servers So whenever you're requesting for a TV show or a movie that you want to watch They try to see based on your geographic location What are the nearest available? Servers that you can probably that they can assign to you so that you can easily get access to that particular video that you want to watch another Very popular algorithm is K nearest neighbor And K nearest neighbor is a supervised machine learning technique With CNN you have some data points that you already know what class it is and you We use these to figure out the data points that that isn't associated to a particular class Based on proximity and Hem is going to tell us more Yes, so there is just a simple Animation for this to sort of understand what key a KNN is trying to do so you already have Classified Labels assigned for some of the data points So you have like a class a and a class B which is the blue and the orange Color dots respectively. So when you have a new data point, which is sort of trying to identify itself to be classified in either Class a or class B. It already has a set of labels assigned So that's how it's different from an unsupervised where there were no labels at all So here trying to figure out do I belong to class a or do I belong to class B and Here again, the K is another hyper parameter that you sort of tune and you're trying to identify Let's say in this example, it's trying to find the nearest three neighbors So the black dot is basically saying, okay, these are my three nearest neighbors of which I have one Which belongs to class a I have two neighbors who belong to class B and it also does further distance Calculations to sort of figure out who am I more closer to and then it associates itself as belonging to I belong to probably class B so that's an example of KNN and a useful analogy to remember this is birds of a feather flock together So again some Applications where KNN might have been used Typically, it's usually in like object detection or pattern recognition kind of systems So in all of these image processing techniques where you're trying to associate like pixel Belonging to which kind of nearest pixels that it should be a grouped under is where like you might have KNNs You have like video streaming services like YouTube So these are all statistically based where they use some kind of KNN to identify where are my nearest Customers and what kind of playlists or videos do they have and what can we recommend based off of that? We also have like a popular one is like gene sequence matching So when you have a lot of pharmaceutical companies who do a lot of research on this they're trying to identify the gene composition and see if they can Classify them into belonging to a given DNA sequence so that they can do it for further research analysis aspects of it And then you also have the credit card application so these are which credit card or bank financial sectors sort of have a KNN to sort out their customers and potentially reach out to like future customers what kind of Users do belonging to which category of customers that I can further reach out to for further analysis So in all of these examples, you are doing a lot of trial and error methods where you're changing different parameters to identify the best performance of these models and that's exactly where ML flow is sort of coming into the picture and Zach is going to talk a little bit more about that Thank you. So As you saw, you know ML flow is Just is also another Project that does hyper parameter tuning. There's many projects that do hyper parameter tuning I believe cat tip is another project that does hyper parameter tuning ML flow is great. It's an open source project. It's you can deploy your models save your models in different clouds and also It's it has a python library that you can embed into your jupyter notebook and It has many great features But just to kind of give you bird's eye view There are three sub projects the sub project that is of interest to us for experiment tracking is just the tracking portion The tracking portion we use that it's basically a server that runs in a container and that server can basically you you'd use the python library to connect to that server and store metadata and You can also hook up that server to Maybe deploy these models into s3 or other cloud storage systems to Save your models and also track The get the get hash and all these other metadata and you can also store your Model along with the visualizations that you generated through maybe a matplot lib along with it as part of your experiment and And it has some great features like Ability to search for the best hyper parameters. It has a python API I'll show an example of it in a later slide Let's kind of look at Just kind of a bird's eye view of like let's say I want to do hyper parameter tuning I want to do k-means and I want to do three different. I want to try three different hyperparameters for K I'll try k with hyperparameter four five and six and let's see which one returns the best results, right? I like to kind of look at it in a kind of like a high level Each blue box is a container and they're all connected to the same data set and Then you have the ML flow tracking server, which I mentioned this was one of the sub projects that we're running in the container and We're gonna be storing all these Metadata and and we can also store the model if you want as well This is just a code snippet Of what you would need to add into your Jupyter notebook in order to take advantage of ML flow You'd have to import the Python library It's just a simple pip install and then you start your run you log your parameter And then you log your your metrics you can have more than one metric You can have more than one parameter in our case. We're just one parameter or one metric For example, if you were to run this on many many experiments and Say you have like hundreds of experiments It's like kind of cumbersome to like look through click click click click click and search through stuff It has a really nice Python API What this statement over here does is out of all the runs that I've done I want you to search The experiment ID zero and use this filter string. The filter string is right over here metric dot R2 Is less than zero point zero four Six right and only returned to me one result. So if there's multiple with the same criteria, just return to me one Slide This is the ML flow UI. I showed you in a previous slide You're doing everything in Python You could do a lot of stuff with the UI as well the UI lets you click on three different experiments and compare them and In here the fonts are not very big But I'm gonna tell you the cake closer is four here It's seven here and it's five here and the metric that we we've said we're getting back a 96% Score on here we're getting a 90 we're getting a 94 So technically four seems to be the the sweet spot for our hyper parameter here So when we're deploying this we'll make sure our case for right next slide Let's say I want to do this in all on Jupiter if I like Jupiter a lot I'll just import ML flow. I'll add ML flow dot and whatever libraries in so if you use a tensor flow You'd use the tensor flow package under ML flow dot Or if you're using other libraries, they have multiple different libraries that you can use And then you would do the Just track it with the log param log metric next slide and When you're doing things like this you want to build containers reusable containers and OpenShift is a great platform to be able to Do built builds and have a container built for you If I don't care about writing Docker files anymore I can just have OpenShift go and point to my source code and it'll do a source to image and Here in an OpenShift template. I can pass in which parameter The hyper parameter is and it'll go and build and run that that job with the Hyper parameter that I want to try Now when I think about this and I want to say you know what maybe that's not good enough I want to do more complex stuff, right? I want to have a visual view of of doing this so For example, let's say I want to have a workflow like a pipeline or something This is like a gif it's like towards the end of the gif, but here's the beginning of the gif so I have Argo here I've already built my OpenShift Build and I have container already, but I'm passing in in Argo workflow. I'm passing in different parameters So when I'm running my my job and it's tracking my experiments in ML flow it can even track my models in ML flow and my models with ML flow can even store my my models in Ceph S3 which is an open source alternative to the S3 that's available by AWS and if you're interested in Looking at this stuff Think you'd be good to see this stuff. Let me So if you wanted to try this at home later You could follow this gist, but I'll just like explain it so you understand why what we're doing here so in here and This is the ammo file what I'm doing is I'm Saying that these are the parameters that I'm going to be passing into this container Here's the image that I'm building that I just built that takes in environment variables One environment variable is very important to know about is the ML flow tracking URI You have to tell it which ML flow instance you you've got deployed That's going to be tracking your metrics and your hyper parameters and then I pass in I make my script as like a command line thing where I can pass in different flags. So In this case, I'm using the example that's provided by ML flow you pass in two parameters and It'll have different results So and here these are this is the workflow. So for example Joanna just presented earlier on kubeflow and kubeflow pipelines and Argos what kubeflow pipelines is based on Basically, it lets you decide like I want you to try these steps and then When these steps are completed, then I want you to try these particular steps so Just to tie this back and once and then you can have like it do something at the end Okay, so That is Argo and ML flow integration um Okay, yes So we're gonna show you a demo because Flies are nice, but what's better than a demo, right? Let's see the real stuff no recordings Let's let's show them And we're gonna be demoing Quran who sits over here. He works on the the set storage Predictions for failure of drives and He's using hyper parameter tuning to do that with ML flow and we're gonna be demonstrating his his work All the tough questions are gonna go to you now Okay, so maybe we'll get the font a little bit bigger Can everybody see that No Oh Yes more more stop it All right, and I'm pretty sure you can see this Automation no more. Yeah, so this was what Karan was working on as part of his intern project so he was basically trying to do like a Seth hardware drive failure prediction so that was the Algorithm that he was training on and he had a few hyper parameters that he needed to fine-tune before feeding it into his model, so That's the generate hyper parameter Python script that he had where you just specify a bunch of your hyper parameters and Once we execute that it just tells you what were the total number of hyper parameters set and it also Generates a YAML file which basically Helps you to create spawn the job based on the number of hyper parameters that you have set in the code and There is an experiment tracking Repository also which we have where you can deploy the ML flow tracking server as well. So here what the Let me just quickly. So there's a small script here Which basically you're trying to specify where exactly is my hyper parameter YAML file that I've just generated and then you're Feeding into where your training data set resides currently and where the training Python file also exists. So these are the Parameters that you're basically passing through and what it does is it basically spawns a new job for every hyperparameter that you have set So when we run this particular script Please don't fail So it basically starts spawning those jobs and as you can see earlier We saw that it said the number of hyper parameters set were 12. So it should spawn 12 different jobs for you And then we can quickly open them So this is where we have our ML flow server set So these are running Slides are go back to Yeah, yeah, so this is the ML flow server So this is the tracking Ui that we were mentioning to you earlier in our slides it this is where you basically log your parameters So we just Run this and it tells you the timestamp. It gives you the username It gives you what was the source of code ran for this particular model And then you have your parameters being passed. So you have some loss functions L2 values, these are just parameters tuned for his specific model that he was looking into and Then you also log different metric values as well As I mentioned earlier, you know, you could Technically run this and then have your pipe have a Python script that will go and query and Based on your criteria, I'll find you the best parameters and then deploy up the model that has those particular parameters And yeah, you can pretty much just to just search over here you type in a particular metric. Let's just say Metric F2 Greater than what this value here Yeah, actually do it here. Yes, right here 0.98 0.93 Searching found 53 results. They found 18 results 12 results and so on so forth now you have like two results that fit this criteria and then you know You can dig into it more But that's just essentially the idea but like I said, whatever you do with the UI you can have it in a Python script and automate that And yeah, and maybe if I want to maybe further Give you a little bit more of a tour here is I can just say, hey, you know These two look like similar stuff, but I want to kind of compare it side-by-side And take a look at it like that. I can do that Yeah Pretty much that's our Presentation We did show you the demo And uh, yeah, we're just gonna give you a little brief overview of what you learned So you've learned the difference between regular software engineering and Machine learning engineering you've learned about some hyper parameters Um, you've learned about unsupervised machine learning supervised machine learning and some examples of them You learned about how to use mo flow to do experiment tracking You've learned how to do put it put it tie it all together and run it in kubernetes on open shift Thank you And uh, if you're interested in contributing this all this stuff is open source One of the best ways to learn is getting involved in open source communities contributing The first link that you see here is the experiment tracking Files for deploying the stuff with templates on open shift That the second link is the golang operator for mo flow if you're interested in contributing in either one We're glad to have more contributions from the community And yeah, I don't know what else to say. Thank you. Thank you Also, there's just a quick announcement So there's going to be a party def con party tomorrow At 7 p.m. And you can collect your tickets for the party today At 4 30 at the registration in case you can't get it today You can also pick them up tomorrow morning at the registration desk Oh, um, does anybody have any questions If anybody has any questions just come on up front and we'll we'll be happy to answer them