Red Lake Tribal members are running across northern Minnesota to show support for those looking for a healthier, sober lifestyle. Mel Meyer met with some of the runners before they took off on the four-day event. Now in its 21st year, the annual Anishinaabe Spirit Run has expanded from its original mission of supporting people, entering, or maintaining sobriety. Organizers also want the run to promote overall health and wellness. We're running for the people, we're running for ourselves, and we're also running to be healed as people, you know, so we can live a good life. It's an important message, one that Neveah Graves could relate to on a personal level. I'm trying to, like, run for my grandma right now because she's going through a hard time. I told her today that I was going to run for her. Graves is one of many youngsters joining the run, and many more will jump in later. It varies from year to year. Some days we start off with maybe 50, and it just sort of builds up as we go along the route. Let's run one of them, guys. After an opening ceremony and reminder of the ground rules, the group headed out of town towards Highway 89. Many members of the running group, Native Thunder, were taking the lead. We asked them to come, a lot of them come because we need good, strong core runners. These runners will help out the others, especially in areas of heavy traffic where safety could become an issue. Derek Barney has already had his fair share of running, but he can take a break every few blocks when they switch out runners. I'm feeling it a little bit, but we got, like, 190 something more miles ago. Barney says the support of the community is helping them move forward. They see it when they drive by, and they honk at us, and it gives us a good feeling. Today's portion of the event will end once the runners reach the Leech Lake Powwow Grounds. In Red Lake, I'm Mel Myer, Lakeland News. The spirit run will end on Saturday at the Fond du Lac Powwow Grounds where the runners will participate in the annual celebration of sobriety. If you've enjoyed this segment of Lakeland News, please consider making a tax-deductible contribution to Lakeland Public Television.


Good morning everyone. I'm just rolling my face out. Don't mind me. So cold. Happy Tuesday everyone Hope you guys are excited for today's vlog. I thought I would just take you guys to hang out with me today I've got some things to do. This was like incredibly annoying of me to do. I'm I'm so sorry getting my nails done I'm getting like a hydration plum My office is getting painted back white in preparation for the closet to be built Which is just oh so exciting the closet is being installed next week now because some items are out of stock Yeah, this morning. I think I'm just gonna take the dogs. It's like seven something I think I'm just gonna take the dogs on a little walk this morning listen to Green lights audio book by Matthew McConaughey. Sounds very exciting for me I kind of want to walk and get coffee if you guys are new here Be sure to subscribe and make a lot of lifestyle vlogs. My name is Kinsey. I'm 23 I live in Dallas, Texas and make a little lifestyle vlogs fit in his health product They style home decor all that stuff So be sure to subscribe if you guys are new here and if this is your first video they are seeing a mind I'm actually curious. Please comment below using my skinny confidential ice roller and like wow does this thing do Wonders okay guys fits once the ice roller. I would roll your face if it wasn't gonna Ruin this I think are you jealous? Mr. Hands up guess who's first birthday is next week. He's gonna turn one I'm getting ready now I'm gonna show you guys what I've been doing for my really simple everyday look always super goop glow screen at first I talked about this so many times but in case you're new here It's a sunscreen, but it like lures out your skin and it leaves you with this beautiful perfect glow It also like evens out your tone, but it's like literally just a sunscreen. I can never tell on camera I think it always looks better in person. That's a good or bad thing and you should be wearing sunscreen every day anyways Which is something I only got good at once I got this product as far as like legitimate just products go That is definitely my number one. Holy girl. I just throw that on the morning. My skin looks so bomb I'm glowing. I think I'm gonna get green tips today. I don't know. I have gel right now I don't like gel I like dip a lot better My dip last like three or four weeks gel literally broke off with them like a day So I have my two recent faves that I've been using I've been using the tire 28 cream blush and then I got the milk makeup Bronzer stick. This is the matte bronzer stick. I Love this thing and then I just buff it out with my favorite brush was for a this is this for Profoundation 64 brush I was using this to apply foundation and then I was using my beauty blender to like blend this stuff out and Then I started using my beauty blender again on foundation and this to blend this stuff out and it looks so bomb I like doing it with a brush for the thick I have more control Than a beauty blender. Just take the tower 28 cream blush and apply that I was always afraid of cream blushes and then I started using them and I love them now Okay, I'm the Charlotte Tilbury at brow fix. I'm just gonna brush my brows out And then I'll use the Charlotte Tilbury pillow talk Mascara this is the makeup look. It's really glue me out for the lighting is trash Really simple glowy dewy easy these sweats are alo this tank top is princess Polly my favorite shoes are from soothies This is no longer available, but it's really comfy. I don't know. I don't know why I haven't been wearing this I think I like forgot about it. It's so cozy especially if I was wearing a sports bra I could like tuck it under and then I think I'm gonna go Oh, where's my normal ring and earring? I love wearing this heart one Maybe I'll throw some hoops on Jennifer Fisher. I don't think I'm gonna wear my bracelet today These would be my sunglasses if it was sunny at all today But I'm gonna take the dogs on a cute little walk. I might walk and grab some coffee as well These are the lip products that I've been loving recently This is the hourglass hint velvet story. Okay. I don't know which one's the name and which one's the color But just you know there you go and then I just have some pretty basic lip gloss time for a walk Oh, I forgot my air pods upstairs throwing my air pods and going on a nice little morning walk Which I actually have been really bad about walking lately and in the book yesterday That I read that I recommend everyone reads She was talking about like building confidence and stuff and how a lot of it's done by like routines to yourself Which I'd already learned that from at my lat I was just thinking of new exercises and ways that I could add that in and I think It would be walks because I used to be really good about walks and they're really good for me And for some reason I just get out of them and obviously I go on walks at the time because of the walks But I mean like the longer Type of walks has going on before. You know anything outside. I just got coffee at the merchant. This is actually called the cowboy love he does Oh, it's really good This is literally the most popular guy on the street today Okay, he's ignoring your eyes. You're cool. I'll push Coco. I'm about to get my nails done This is what they currently look like listen to doctor will close podcast with Dr. Nicole Apera. I think that's how you pronounce your name She's the doctor and author of the book that I was talking about earlier and I love her I'll let you guys know how I feel but Sav and I both read that book yesterday and then the day before we read Your brain at all is always thinking. What was it? Dr. Amon's book? I already talked about it Basically, we read two books in two days because it's Hawkerl summer Hawkerl's read and we are growing. We're 23 We're 23. I'm in a rush. My nail appointment ran very late. Here are my nails. They're really cute I just changed the tip to a green tip, which I love. I'm on my way to I am med spa This is where I go for botox. I get preventative botox I do have a code if you guys go there. It's in Dallas It's at cider Plaza, but I do have a code if you guys want to go I will put it on the screen You guys get 20% off. I've never had any interest in like getting lip injections or anything like that But they've been talking to me about getting this like hydration pump is what they call it Essentially, it's like a really low level of lip injection just for my top lip And honestly, I mean when opportunities present themselves like who am I to say no, you know, it's not going to look crazy It's going to be like a very low dose Surrender, I don't I don't know my camera just died. So we're on my phone, but here are the nails I love them guys. Also neon makes me look tanner Which is great Here I am guys And I'm gonna get you to open just a little bit Side okay, we're gonna do the first couple like the anticipation.  Yeah, keep them kind of loose a little bit. Yep, right there We're near the first one. Okay No, that was not bad at all I feel like that's comparable to botox like very comparable. Yeah, but also like my lips are not so exactly That's a little different down in the bottom. Is it worse? I think it's a little bit worse But most people think the upper is a little worse. So you kind of tell me you're I'm gonna have you break through it though I do that in And exhale, exhale, exhale good What do you think compared to the top? I don't know because the left side hurt more than the right side to me but I think like maybe the initial hurt a little bit worse, but like the longer I don't feel like it was just bad Out my lips are still very numb. I've never gotten any sort of like lip filler It's obviously like really numb and swollen right now They'll like chill out and like become more me. I have like two more weeks of this But I really like them. Honestly, they're really like Normal looking like they're not too crazy Which is what I was going for but I just wanted to take you guys along It wasn't that painful this side hurt worse than this side or this side hurt worse than this side Um, I barely got any in my bottom for those who are going to ask. I got about half a syringe I think it was like 60 of the syringe Here we are everyone. I just picked up some paint the black wall in my office That's going to be my closet room is getting painted today. So the closet room is officially Starting to come together as of today. The weather is like gross right now. I'm not into it Also, I'm supposed to work out and I realize they can't work out because you're not supposed to sweat for 24 hours after getting lip injections, which is unfortunate So at least I wanted to walk this morning, but I was really looking forward to my workout I love Texas because the men there were so kind so nice insisted on carrying it to my car I mean, there's just really no place like Texas. All right guys. I just Took down all the paintings. We never even fixed the paint for when we painted this last summer But if you didn't know I'm making this room into a custom closet and this wall obviously just needs to go back to being white It's going to be covered But like the very top won't be and I just don't want to have to deal with it later I'd rather just do it now. So it's getting painted today and as for all of the art Um, I have a ton of stuff over here. It's going to go elsewhere in my house. So We're figuring that out. I got about just about to make one of my go-to solids Okay, the secret to a good filling salad Hummus avocado and then mary's crackers You can find mary's crackers at like health food stores normally. I just like crumble them on the salad They're really good and they really add something to your salads guys. Just had lunch. It was really good I'm gonna get back to work. Um, I'm having this strawberry cold brew and I got nut pods I know that I've had these in my house before because my mom is like all into this stuff But I was reminded about them because julie is obsessed with them right now. So I'm making some coffee Update on how my lips are feeling Definitely like numb and sore a little bit But I'm gonna put my before and after on the screen. I texted it to sav and if you guys don't know sav is like my best friend since Middle school high school how it's been like 10 years and she like doesn't even really wear makeup But she's like super supernatural and even she was like, oh my god, I've never seen lip injections that like I've liked before this girl's an artist So that's a good sign we're back on the vlog camera I feel so weird like my lips are still kind of numb But they're gonna get sore and I feel like I'm in the in-between of them So don't mind that today's vlog but on my computer for the past few hours And I'm now going to go see the new wall because it's been painted Oh my gosh guys Whoa It feels so different Like so different back to when I first bought this house. I've been sitting here working Recording some stuff for tomorrow's episode which will already be out by the time you guys have seen this Basically, I just if you guys have ever felt lost or confused in your 20s or really just ever in life I have no idea what you're doing and so confused and um You know that you're changing a lot and you're just like confused On life on who you are on who you want to be and what you want to do Um tomorrow's episode is for you. It'll be linked below I think it's called like feeling lost in your 20s and then Lauren Elizabeth and I also recap book club You know, it's so crazy though. I remember this like literally five minutes ago An old mentor of mine in LA told me that you should reinvent yourself like every three years And she's like you're gonna be a different person like every three years And it's not that I didn't like who I was like I loved who I was especially at that time But in that time of life that worked then and this is what works now So it was like encouraging to remember That's like normal There was also something that I saw on instagram the other day about like one of my favorite instagram accounts and Oh, my cousin. I was literally tell me why So weird I was just thinking about something with someone and then texted me Exactly what I was thinking. We hadn't even talked about it. It's all Why but basically it was like emotional maturity. You know what? Let me like actually find it for you emotional maturity is allowing yourself to change and not becoming attached to a strict or fixed idea of who you are That's good. Alyssa my cousin is about taking up and we're going to get Thai food for dinner Very exciting things happening update on my lips. They're kind of sore. They're not really numb anymore But they're definitely like a little bit sore We're at uptown pad thai. I think is what it's called. Um, it's really cute. This is like, oh my god Have you been to zennas before downtown Wait, I think they have multiple locations. I've only been to the one downtown But we used to go at like 3 a.m. And with Alyssa obviously we used to go like 3 a.m. All the time and it was so much fun Anyways, this is looks bomb. It looks very good. You're out of mommy Do you not want investors because you just want more control like what is the what's the benefit And the cost so investors are awesome in one day. We will need them But I want to retain my equity until I don't need


I am Federico, I'm the UX designer of the Moodle Community Team. I'm going to give the updates of Moodle Academy. So in the Moodle Academy during this spring, we have been evolving the idea of the upcoming events, filter and functionality, because in the last spring we were talking about a really draft of an idea, but we have been evolving this idea in this spring. We were focusing also in the calendar page because a lot of events are coming to Moodle Academy. We wanted to give the possibility to the user to filter all the events that are in the screen from the course, hosted by language and learning pathways. So they have these four kind of filters and when they close the filter window, it appears the amount of filters that has been applied and also a clear all button for the user. Next slide, please. Also in the functionality, in the other kind of filtering, we have the format of how the user sees these events and we wanted also to give the user a possibility of filtering the events by past events. So now the ability for the user to see, for example, the recordings or webinars that already passed and we want to change the structure of the event card. So when an event is passed, we can show links for the recordings and feedback information for the user. We have the Figma link and the tracker ticket. Next slide, please. And also regarding the calendar page, we want to change the structure of how an element is shown. So we are kind of clearing a bit because when we have different events in the same page, there's a lot of scrolls, so it seems like a lot of information on the page. So we decided to clear some of the structure instead of the card element. So we have not the whole description, but the user can show all the description and hide it and also the header key of the information. We wanted to make it a bit more clear, so we create a different structure for the metadata for the element and also the buttons so are more consistent across all the Moodle Academy websites. And overall, we also were working with more improvements with the Moodle 4.0, like a lot of tickets, solving small issues. That has been for this sprinting Moodle Academy. So quick demo. If AI is online, she'll probably take over and explain what the UI changes have happened and I'll just take over after. Thank you for that already. Hello, everyone. I'm Aya, a developer in the community team, and I will share my screen to quickly take you through a demo of what has been done this sprint. So let me share my screen quickly. Hopefully you can all see it now. No. No. Yes. One second. Yes, maybe now. Okay, perfect. So the major issue of this sprint was the issue reported in the forums. I will just play this video quickly. The forum, it has been reported that the forum's list, the discussion list was overflowing the width of the phone and basically performing a strange view on the mobile screens. So this has been solved as an MDL issue and a solution has been proposed there. It's currently working on our Moodle.org and waiting to be tested to be integrated in the elements. So it's currently fixed in the Moodle.org site. You can see it's performing well in different screen sizes. It's not overflowing as it used to be and the issue reported. So yeah, this was the first issue. And then... Sorry about that. And the other thing that was on this sprint is the logo update. We now have the updated logo with the TM, the trademark note there in all of our .org sites. So the lang site, stat site, also downloads, and jara. And another change that was made in the Get Involved page in Moodle.org. We have amended the sections a little bit. So we removed some of the duplicated links and we amended the section. So it includes visible and easy to access links for the user. We've added a documentation link and the Moodle user association link, which takes you to pages that explain what exactly you need to do for these elements.  So the events in the home page of Moodle.org, it was showing a button or an information here that was saying event. It was not visible on what exactly to do. So this has been removed. And another thing was a solution for the hyperlinks for Moodle.org. We had some complaints about the links in the forums not being really visible. So we introduced a solution that it's now underlined and it's working fine in the Moodle.org. We've also updated the logo for the Moodle user association. You can see it here. Yeah. And yeah, basically that's it over to you early. Thank you. That's great. I think we can go back to the slides. Yeah. So a lot of changes in terms of UI, all based on feedback we've had from the launch on the 6th of October. The other things that have progressed, we've mentioned the plugins directly, kind of project and discovery phase earlier. So we've had those stakeholder workshops to enable us to plan for the next increments improvement. So the next few weeks will be concentrated around the results findings and planning around that. We've also had other fixes and these are just a few. There are other things. There was something that's not in here about improving security forms. For example, some support tickets happened as well, ensuring that Media Weekly doesn't perform requests to external resources. That's something that's been reported internally and fixed. I've mentioned the MUA interviews and presenting to the working group when I talked about the goals and also the long and sats model, the org sites have been updated to 4.1. So that's it. Next. Oops. Just click back here. Some of the fun things, I guess, support and engagement, what the community is about really. So we've had the parties awarded for the QA testers. Early birth challenge finished just at the start of the last print. We had a total of 248 plugins receiving an early birth 4.1 awards. That's about 228 of them were actual plugins that were in the database that were fully reviewed. The blog about the 2,000 plugins, which we well surpassed since, has been published as well with marketing. And we've launched a small competition, Share Your Moodle Love, so if you know anybody who's got really nice courses that they want to share, now it's called Designer Moodle, link them to our competition and the competition center, which is now live on Moodle.org and we'll be trying to run some competitions to engage users as we go as well. So that's it.


Income tax 2022-2023, rental property, special situations not rented for profit and example of change to rental use. Let's do some wealth preservation with some tax preparation. Most of this information comes from publication 527 residential rental property including rental of vacation homes tax year 2022. You can find on the IRS website, irs.gov, irs.gov. Looking at the income tax formula, we're focused on line one income. Remember on the first half of the income tax formula is in essence an income statement, but just an outline other forms and schedules flowing into these line items. One of those, the Schedule E, an income statement in and of itself in essence having rental income minus rental expenses. The net rental income flowing into line one income of our income tax formula. So now we're talking about renting part of property. So in prior presentations, we've been focusing mainly on the more straightforward types of scenarios where you have a separate piece of rental property that's 100% used for rental property. And now that we've gotten some ideas and the general concepts down, which will typically be applicable to more complex scenarios as well. We'll take a look at some more of those kind of wrinkles, some more complexities that are thrown into the mix. So now we have renting part of property as an added complexity. So if you rent part of your property, you must divide certain expenses between the part of the property used for rental purposes and the part of the property used for personal purposes as though you actually have two separate pieces of property. So in other words, it's nice and easy when you have a separate piece of property that is used 100% for rental use because that makes the bookkeeping nice and easy so that we can separate personal and business usages. However, if we have a situation where we have a one piece of property, we're using it partially for rent, partially for personal. Then we have the situation that we have to parse out the business and the personal clearly usually most commonly on the expense side of things because the rental income will be the rental income. It's going to be coming from the rental side of things. The expenses, however, might need to be divvied up if they're for the entire property so that we can allocate the proper amount to the rental portion, which may be a deductible portion. So you can deduct the expenses related to the part of the property used for rental expenses such as home mortgage interest and real estate taxes as rental expenses on Schedule E form 1040. Now this also becomes a little bit more complex because when we think about some types of deductions, we might actually get a deduction for them if it was our personal residence on a Schedule A, which is a little bit strange because in an income tax type of system, we would expect the deductions that would be natural to the income tax system are those that we needed to consume in order to generate the revenue so that we're taxed on the net income, not on the gross income. But when we look at personal taxes, like on the Schedule A, the itemized deductions, a lot of those don't follow the general concept rule. There are other reasons. There's political reasons or whatever they were thinking that they put all this other stuff on there, right? So the real estate taxes that you might be able to deduct on the Schedule A aren't because it's business property, but rather for whatever other reason they put that on there. And so same with the interest. So the interest, which isn't for your rental property, but for your personal home, you would think wouldn't be deductible naturally just like interest on your personal car loan or something isn't usually deductible. But they have those as deductions on the Schedule A. So now you're in a situation of saying, okay, if I have one piece of property, I live in part of it, I rent part of it, then I might be able to deduct the real estate taxes. I'd have to divvy it out between the two areas that I might get a benefit from it, right? On the real estate Schedule E and on possibly the Schedule A kind of situation. Alright, you can also deduct as rental expenses a portion of other expenses that are normally deduct a non deductible personal expenses such as expenses for electricity or painting the outside of the house. So if you have your own home, then you can't deduct personal stuff on the home except for the mortgage interest and the property tax for whatever reason, right? You can't paint the home and get a deduction for the painting of the home. Why? Because painting the home is a personal expenditure, not a business expenditure, therefore not deductible. But if you rent part of the home, now if you painted the home, you would expect that part of the painting process was for the rental of the home and therefore a business or renting type expense and therefore possibly deductible on the Schedule E. Alright, there is no change in the types of expenses deductible for the personal use part of your property. Generally, these expenses may be deducted only if you itemize your deductions on Schedule A. So for the personal side, you only get the benefit if you itemize, which means your itemized deductions typically have to be greater than the standard deduction to get a benefit from them. You can't deduct any part of the cost of the first phone line, even if your tenants have unlimited use of it. You don't have to divide the expenses that belong only to the rental part of your property. For example, if you paint a room that you rent or pay premiums for liability insurance in connection with renting a room in your home, your entire cost is a rental expense. In other words, if you've got a home, you rented part of the home. If you paint the whole outside of the house, the whole house, well then you're going to have to divvy up the cost of that painting project to the rental and the personal part. But if you rent or if you paint just the inside of the place that you're renting, the part of the home that you are renting or the part of the place that you're renting, then obviously that whole cost is for the rental portion and therefore you would be able to deduct the whole thing. The problem occurs is when we have costs that are for the whole unit, the whole structure that we need to be allocating between personal and business because we use it partially for personal, partially for business. So if you install a second phone line strictly for your tenants' use, all the cost of the second line is deductible as a rental expense. You can deduct depreciation on the part of the house used for rental purposes as well as on the furniture and equipment you use for rental purposes. So how to divide expenses. So if and expense. So now we've got this issue. We see the issue. How are we going to do this? How are we going to get this done? We're going to need some kind of ratio analysis or something to be breaking these expenses out between rental and personal. Let's think about it. If an expense is for both rental use and personal use such as mortgage interest or heat for the entire house, you must divide the expenses between rental use and personal use. That's the issue. You can use any reasonable method for dividing the expense. Obviously reasonable being a key term and one that's not very specific. So could you give me some suggestions on a reasonable method? So it may be reasonable to divide the cost of some items, for example, water based on the number of people using them. So now we've got, if you've ever dealt with managerial accounting kind of concepts, we have this like activity based concept in that we have to use some kind of ratio in order to be breaking these different expenses out. And the first thing that probably comes to mind is to use like a square footage ratio. This is the square foot of the rental versus the non rental, which makes sense. And that's a good reasonable method to use. But in some cases there might be other reasonable methods depending on the thing that you're talking about. So for example, if you're talking about water, you would expect maybe something that would be more accurate than the square footage would be the number of people that are consumed. That are consuming water in the home. And so you can take a ratio of, like if you're the homeowner, then you're only one person. And then you have renters, which are a whole family of seven people, even if they're squished into a small place for whatever reason. You would think that they might use a lot more water than the ratio of their square footage to the total square footage, right? So you might say you might use a different ratio if it's an appropriate activity base that would drive an appropriate ratio. So the two most common methods for dividing expenses are one, the number of rooms in your home and two, the square footage of your home. Example, you rent a room in your house. The room is 12 by 15 feet or 180 square feet. So your entire house is 1,800 square feet of floor space. You can deduct as rental expense 10% of any expense that must be divided between rental use and personal use. Because we're going to say it's the 180 square feet divided by the total of 1,800. And therefore, all of this stuff, like the utility bill or whatever, possibly the painting of the house, the whole house, not the one room, you break out 10% to the rental. So if you're heating bill for the year, for the year for the entire house was 660, 600 times 10%, is a rental expense. The balance 450 is personal expense that you can't deduct. Duplex, a common situation is the duplex where you live in one unit and rent out the other. That's a nice situation. So certain expenses apply to the entire property such as mortgage interest and real estate taxes and must be split to determine rental and personal expenses. So if you have a duplex, then it might be more likely that some of the stuff like utility bills and stuff is going to be more easily broken out. But some will still not be broken out, such as the mortgage interest because you bought the entire duplex as one unit and you're paying loan. On the entire property and the property taxes, which still need to be broken out between personal and the rental. And then they can possibly be deducted on each because of the personal side would be schedule A, rental side schedule E. Example, you own a duplex and live in one half renting out the other half. Both units are approximately the same size.  You can deduct 5,000 mortgage interest and 1000 real estate taxes on schedule E because it's a duplex and you would expect it would be cut in half if you're living in one half of the duplex. So if you itemize your deductions, deductions include the other 5,000 mortgage interest and 1000 real estate taxes when figuring the amount you can deduct on schedule A. Because they're personal, which means you wouldn't normally get to deduct them except that they're weird schedule A items which you may be able to deduct there. Not rented for profit. So if you don't rent your property to make a profit, you can't deduct rental expenses in excess of the amount of your rental income. So now you're renting the property, but maybe you're not looking to get a gain on the property. Well, you're still kind of renting it, but what the IRS is going to be clearly most concerned with us is this loss situation. Because if you're not looking to get a profit and you have these losses and then you're getting a tax benefit from the losses by taking the losses against other income, that's where the IRS obviously is skeptical all the time on the losses. They want your income. If you're making money, then they want a piece. If you're losing money, that's on you. That's on you, says the IRS. Don't be asking me for putting money in there. Anyways, you can't deduct a loss or carry forward to the next year any rental expenses that are more than your rental income for the year. So where to report? Report your not-for-profit rental income on Schedule 1, Form 1040, Align 8J. So if you itemize your deductions and close your mortgage interest, if you use the property as your main home or second home, real estate taxes and casually losses from your not-for-profit rental activity when figuring the amount you can deduct on Schedule A. Presumption of profit. So if your rental income is more than your rental expenses for at least three years out of a period of five consecutive years, you are presumed to be renting your property to make a profit. Now this is similar to a situation where if you have a business, a Schedule C business, the IRS is skeptical as to whether it's a hobby or not. In the classic example is that horse race situation where you had rich people that had this really expensive hobby of horse racing, but they kept on running these big losses. So you might have income, but horse racing apparently is quite expensive to own a horse, I guess. And so you could have these big losses and the IRS is like, well, look, that's a hobby and we're not going to pay for it. We don't want you to be able to take the losses against other income. But other businesses run losses, obviously. In the first three years, it's quite common to have a loss even if you have a profit motive. So then the presumption then is usually on the IRS's side to prove that it's a hobby rather than a loss. If you're saying I'm doing this for profit even though I'm running losses, then it's on the IRS to kind of prove otherwise unless you go over a certain threshold. If you have three or more three years out of more than three years of losses, at some point the IRS is going to put the presumption more on you. So we have a similar kind of situation here. Your presumption is for profit. And which is a common scenario with rental property in that losses are another big issue that we've been talking about a lot of times because you have the property that could be going up in value of the property. And so they might be thinking, I'm going to try to take losses as the property accumulates in value, but you have a similar situation here where the IRS is going to say give you a presumption that it's for profit. If you say it's for profit, and then if you go over a certain number of years of losses, then the IRS is going to say now it's on you to prove that it's for profit. It's a general kind of idea. So once again, if your rental income is more than your rental expenses for at least three years out of the five-year period, meaning you had income rather than losses that you were taking against other income, consecutive years you are presumed to be renting your property to make a profit. Okay, postponing decision. If you are starting your rental activity and don't have three years showing profit, you can elect to have the presumption made after you have the five years of experience required by the test. So you may choose to postpone the decision of whether the rental is for profit by filing form 5213. You must file form 5213 within three years after the due date of your return determined within extensions for the year in which you first carried on the activity, or if earlier within 60 days after receiving written notice from the IRS proposing the disallowed deductions attributable to the activity. Alright, more information. For more information about the rules for an activity not engaged for profit, you can see not-for-profit activities in Chapter 1 of Publication 535.


1% richest Indians capture 20% of national income in 2014. This is up from about 6% in the mid-1980s. And we observe before, from 1938 to the mid-1980s, a sharp reduction in the share of income held by the top 1% from about a bit more than 21% or around 21% to 6% due to a series of shocks on the owners of capital due also to the implementation of regulation policies so nationalizations of many sectors of the economy including banking, transportation, including a lot of sectors of the economy along with very high tax rates, so tax progressivity was very high in the 60s, 70s, up to the early 80s with the top marginal income tax rate going as high as 97% with an inheritance tax implemented for a short period of time during this period and with in fact an explicit objective by Nero and by his followers to limit the power of the economic elite so this was clearly one of the objectives of the government and so this period which starts at the beginning of the Second World War which is prolonged after independence actually follows a period of rise in income inequality from the start of the Indian income tax from 1922 to 1938 during which we witness, we observe a rise in inequality which is likely to be due to economic transformations at the time which favored capitalists over workers so basically from 1952 to the mid 80s clearly you see a reduction in top income levels so there is a reduction in income so negative growth for individuals at the very top of the social ladder or of the income distribution as economists say and growth mostly benefited to the bottom 50% the middle income group, the middle 40% from 1950 to the mid 1980s now of course overall growth was much lower or slightly lower in the 70s and the 80s and 90s than in the next period but in the next period if growth rate were higher it was essentially captured this new growth was essentially captured by the top of the distribution the top 10%, the top 1% in fact from 1980 to 2014 the top 0% the richest 0% Indians captured as much growth as the entire bottom 50% combined this highlights the very unequal nature of shining India of the growth policies implemented over the past decades and in fact the fact that basically the majority of the population did not experience the kinds of growth rate that we hear in the media regularly these growth rates in fact the growth rate that they experienced were lower than the average basically what we see in this data goes contrary to this intuition in public discourse in the mind of a lot of people that there was high middle class growth I mean the middle 40% grew at a faster rate in the 2000 and the 2010s faster at a faster rate than in the 90s than in the 80s however what we show in this paper is that it grew much less than the average and in fact shining India essentially happened at the very top of the distribution so then the question we ask is how do we define a middle class this is not an easy task to do if we define it in a very simple way there are many different ways to define it but in a very simple term as the individuals between above the poorest half and below the richest 10% so the middle 40% well these group of individuals didn't benefit a lot from the high growth years of the post 2000s


The Marxist and classical liberal theories of history both offer distinct elements which indicate that a marriage of sorts may be profitable to both parties. Both theories explain change over time with reference to class formation and conflict. But what exactly are these classes, no matter how they may form or come into conflict? Who exactly compose these great social blobs, and why did those individuals do what they did? Brought down to the level of history from below, classical liberalism and Marxism must both approach their subject from the real, lived experience of individuals. This is Liberty Chronicles, a project of Libertarianism.org. I'm Anthony Comegna. For the Marxists, social classes are based on access to the means of production. When the super wealthy and disproportionately powerful few constantly gain at the expense of the many, the many will eventually try to offset starvation by eating the rich. According to the liberals, change is the result of disparate power relationships, splitting the population between those seeking a great sphere of personal liberty and those using force, fraud or threats to manipulate the choices of others. Social classes are based on access to the means of coercion. It's my hope that we can use methodological individualism to find a point of union between the modern Marxist perspective that does emphasize the need to construct history from the real, lived experience of acting human beings, and the modern Libertarian perspective, which accepts individualism as perhaps the key principle of social science. Methodological individualism is the principle that only individuals act. Only individuals consciously apply means toward the fulfillment of ends, and really all this means is that whenever we investigate society or any other collective unit, we must do so through the experiences and actions of the individuals which compose it. The larger society does not think, it does not reason, it does not decide anything. For more, let's turn to the man himself, Ludwig von Mises. This methodological individualism has been vehemently attacked by various metaphysical schools and disparaged as a nominalistic fallacy. The notion of an individual, say the critics, is an empty abstraction. Real man is necessarily always a member of a social whole. It is even impossible to imagine the existence of a man separated from the rest of mankind and not connected with society. Man as man is the product of a social evolution. His most eminent feature, reason, could only emerge within the framework of social mutuality. There is no thinking which does not depend on the individual. There is no thinking which does not depend on the concepts and notions of language. But speech is manifestly a social phenomenon. Man is always the member of a collective. As the whole is both logically and temporally prior to its parts or members, the study of the individual is posterior to the study of society. The only adequate method for the scientific treatment of human problems is the method of universalism or collectivism. Now, the controversy whether the whole or its parts are logically prior is vain. Logically, the notions of a whole and its parts are correlative. As logical concepts, they are both apart from time. It is uncontested that in the sphere of human action, social entities have real existence. Nobody ventures to deny that nations, states, municipalities, parties, religious communities are real factors determining the course of human events. Methodological individualism, far from contesting the significance of such collective holes, considers it as one of its main tasks to describe and to analyze their becoming and their disappearing, their changing structures, and their operation. And it chooses the only method fitted to solve this problem satisfactorily. First, we must realize that all actions are performed by individuals. A collective operates always through the intermediary of one or several individuals whose actions are related to the collective as the secondary source. It is the meaning which the acting individuals and all those who are touched by their action attribute to an action that determines its character. It is the meaning that marks one action as the action of an individual and another action as the action of the state or of the municipality. The hangman, not the state, executes a criminal. It is the meaning of those concerned that discerns in the hangman's action an action of the state. A group of armed men occupies a place. It is the meaning of those concerned which imputes this occupation not to the officers and soldiers on the spot, but to their nation. If we scrutinize the meaning of the various actions performed by individuals, we must necessarily learn everything about the actions of collective holes. For a social collective has no existence and reality outside of the individual members' actions. The life of a collective is lived in the actions of the individuals constituting its body. There is no social collective conceivable which is not operative in the actions of some individuals. The reality of a social integer consists in its directing and releasing definite actions on the part of individuals. Thus, the way to a cognition of collective holes is through an analysis of the individual's actions. As a thinking and acting being, man emerges from his pre-human existence already as a social being. The evolution of reason, language and cooperation is the outcome of the same process. They were inseparably and necessarily linked together, but this process took place in individuals. It consisted in changes in the behavior of individuals. There is no other substance in which it occurred than the individuals. There is no substratum of society other than the actions of individuals. That there are nations, states and churches. That there is social cooperation under the division of labor becomes discernible only in the actions of certain individuals. Nobody ever perceived a nation without perceiving its members. In this sense, one may say that a social collective comes into being through the actions of individuals. That does not mean that the individual is temporally antecedent. It merely means that definite actions of individuals constitute the collective. The problems raised by the multiplicity of coexisting social units and their mutual antagonisms can be solved only by methodological individualism. For libertarians, this concept has been a cornerstone of our thought in some sense forever. The exact meaning, practice and implications of individualism took time to develop for sure. But since there has been anything distinguishable as a liberal tradition, individuals, their interests and their fundamental moral rights have always been at its center. For Marxists, however, accepting and practicing methodological individualism may be difficult. Though I should stress that the very best historians I've encountered, no matter what their official school of thought, have always been methodological individualists. In recent decades, as structuralism has fallen far out of favor, historians reared on Marxism have almost become methodological individualists by default. Today, Marxist work is dominated by history from below. They produce histories for those people whom power has disenfranchised, denuded and silenced. Whatever you might think of modern Marxist historians or the validity of their full programs, the Marxist contributions to history from below offer as much insight and value to libertarians as we and our strict individualism have to offer them. But if only individuals act, why is there so much of our history about collective entities? If we recognize that the state is nothing more than a bunch of people doing stuff, how do we tell nice, neat, concise, pointed narratives about the past? Well, maybe history simply isn't neat. I asked the editor of Cato Unbound, historian Jason Kuznicki, about how the methodological individualist studies and reconstructs the past. History is not just a story of events. The weather is a story of events. History is a story of events plus motivations. History conventionally begins with writing because writing gives us some insight into motivations. Without writing, we have very little insight into motivations. We have some speculation about animal behavior, but it's very fragmentary and partial, and we don't really know what the animals themselves claim to be doing because they don't have speech. When we have writing, we have ideas from people that give us some insight into why they did what they did. That insight is not perfect. Sometimes it is fragmentary. Sometimes it is a product of self-delusion. Sometimes it is full of lies, but at least it's something to go on. So we begin history with writing because what we're trying to do is gain insight into human motivations over time. History is not just random, but it is incredibly complex. It takes an awful lot of work to reconstruct what you would say is a convincing explanation for why people did what they did. It just takes it, especially if you're doing history from below. It's a huge evidential burden to build up what is actually representative of the past, but that doesn't mean that it's impossible. It just means we have our work cut out for us, basically. It is complex, and often it leads to arguments that are never satisfyingly resolved. Now, there was an enormous desire for some sort of simplifying narrative theory of history, some way to reduce all this complicated messy human stuff into a set of laws that would explain the course of history in the same way that Newtonian mechanics explains the motion of the planets very, very well. I think that this urge toward a comprehensive explanatory science of history finds particularly clear expression in Marx. This is what Marx was really trying to do. He wanted to find a way to reduce history to its lowest terms, to make it tractable, to make it almost calculable, if you will. He was himself somewhat realistic about these prospects and acknowledged that there would still be complications and difficulties. He thought he had found exactly this type of explanation for historical events. Now we can say no, he did not, but at the time he was quite confident about it and managed to convince a lot of people. Again, it seems that the main conceptual tool that the Marxists and those before them, the classical liberals, were lacking was this strict methodological individualism. They did not, certainly the Marxists, but even the liberals did not, generally speaking, treat the individual as the fundamental unit of social analysis. What exactly does that mean and how do we know if we're doing that? We can't predict in advance how an individual will act and we can't look at the traits that they bear with them going into an event to make reliable inferences about how they will act in the moment. Now, sometimes those inferences appear to be verified, but then other times they're not. When you have that kind of evidentiary pattern where you can verify something sometimes, but then also it gets falsified a lot, you don't have a valid causal theory. This is not science, this is an attempt that has failed. You will know that you're doing methodological individualism when you do not simply refer to categories to try to explain human behavior, but instead make every effort to listen to what the person claimed that they were doing. It's very important to ask why people took actions and to listen to their own stated motivations. Now, those motivations may be brushed up, they may be self-serving, they may be delusional, but it's important to listen to them anyway and to tell them as part of the story. Teachers and professors are quite right to warn their students against using the passive voice when writing. Active voice, for example, Thomas Jefferson sold slaves, clearly and precisely establishes who is doing what to whom. But with passive voice, slaves were sold, it is entirely unclear who actually acted here. Who sold these slaves? Who captured them in the first place? They simply were captured, I guess, you wipe your hands and move on to the next page. But methodological individualism forces the historian to discover the root causes of human action. We have to dig down as far as possible through the fog and muck of society, cut through the cobweb-like narratives of countless historians from above and reinterpret the past as individuals actually lived it and acted. Someone sold those people into slavery, someone bought and transported them, someone repurchased them and perhaps reshipped them, and someone yet still kept them in torturous bondage until worked into dust. Plenty of others allowed all of this to happen to fellow human beings in their midst. We owe it to the victims to find out who did what to whom and why. The individualist social scientist is no passive observer of aggregated events, nor is she the collection and distribution hub for group entities and identities. Instead, the individualist historian lets past actors speak for themselves as themselves. If historical voice was somehow denied them, then the historian's task is one of salvage and recovery. Liberty Chronicles is a project of Libertarianism.org. It is produced by Test Terrible. To learn more about Liberty Chronicles, visit Libertarianism.org.


Hi, my name is Daniel Valdivia, and I'm an engineer here at Minayo, and today I'm going to talk to you about writing machine learning pipelines against object storage and the reason why you should be thinking about doing it that way, right? So I'm going to be introducing you first to the part of the problem about how Traditionally organizations are writing machine learning models and using them in production and then we'll see what are the cons about that model, right? So let's get started So this is the problem that Is seen around in the industry, right? So traditionally you have a machine learning engineer and these guys will have some really fancy machines, right? They they have some nice CPU some local search and sometimes even GPUs to speed up training depending whether they're doing traditional machine learning or perhaps deep learning And then they will sometimes they cannot feel the whole data set right until the machine So they take a subset of that data, right? So they can train because of course you you are experimenting your testing stuff You want to make sure that you got it right and then they pick the framework of their choice, right? Sometimes there they want to go with TensorFlow, MXNet, PyTorch, the most popular ones They're working perhaps on on a Jupyter notebook on a Zeppelin notebook or perhaps they're just building the Final code on top of it and this is so far this so far. It's great Because it's actually Encourages the directive building of the application and this is where it still look resembles a lot of traditional application development Now the problem with this is that usually when these engineers are done building the application The machine learning model being treated as an application They they throw it over the wall, right and who catches this? That's the DevOps and then the DevOps has the mission to take this application and go figure it out Okay, what data do I need to bring to train this? What what's the file how I'm gonna grow on it? I'm gonna be doing this on top of virtual machines on top of containers Because the infrastructure, right? I'm gonna be picking GPUs CPUs, right? So these students separate to its entirely separate worlds and then it completely Isolates and this this interest a variety of problems because for example, let's look at it this way Let's say you're training a very large problem like image in it So image net is a image recognition project, right? So very very firmness But if you want to train this yourself or build something on top of it, the data set is 1.31 terabytes in size, right? So this is fairly large even for the average engineer machine, right? So and it has 14 million images. So let's say a machine learning engineer that wants to work with this data set You know probably the data won't fit on his computer and now when when it comes to production Let's say the union manages to feed this data setting this machine and wants to train train like this in production, right to serve the model Right this these notes notes with did have you know fancy GPUs. Sometimes they have tensor processing units or also known as TPUs This takes too much time to download imagine You're just loading the 1.31 terabytes of capacity of data into the algorithm And then the first part of the pipeline broke and then you have to do it all over again, right? So this is one of the problems that traditional machine learning development and deployment strategies are Gonna face especially working with large data sets And it doesn't have to be this way if from day one you you have a healthy development cycle You can actually go around many of these problems So let's let's zoom in and look into the stages of building a machine learning pipeline, right? So On why this is quite important, right? So I was telling you that you know if you're training your Machine learning algorithms doesn't matter if you're doing some deep learning like in my examples I'm putting some tensorflow logos. You can imagine this as well with any other framework, right? We're so usually compute tends to be quite pricier, right? So in my case if I'm getting some VMs with GPUs or CPUs on the cloud provider Or just procuring that harbor for my data center these machines that are definitely gonna be way more pricey, right? So having them block on doing basic operations like loading data in and out It's just too expensive, right? And this gets even worse if you're using an even nicer machine on the cloud like the ones that use TPUs those machines Are going to be way pricier but comparing these type of infrastructure to storage infrastructure, right? In total infrastructure can be more plentiful and it's actually cheaper to operate and host so This is where the advantage of actually designing your machine learning pipelines against object storage comes into place, right? You can design everything where you have a very large data lake of data sets, right? You're at any given time when you need to train Let's say you keep your data on premise and then you want to train on the cloud when you need only when you need to train It's because then you get access to burstable compute, right? Or maybe some other Situation on your organization. Maybe the compute is constrained, right? You have to time-share it or something like that, right? So then you want to make sure that when it's your time to crunch you you don't spend time Actually loading data. You just want to go ahead and crunch the data, right? Perhaps you're showing this infrastructure for some other purposes, right? So that's it's very important that you have a very clean separation of the infrastructure And I mean the storage infrastructure is great if it's running object storage because it can be built very large-scale at a very low cost so Summing into machine learning pipelines usually how they're built is you know an engineer goes and he extracts some data, right? He extracts it out of somewhere and then He cleans it up He then pre-processed the data for whatever format the algorithm that he plans to use will will take in, right? So perhaps he's training some in-match recognition. He'll he'll grab this and put it translated to some zero some once And then he'll crunch it to some algorithm, right? So that's very important now will train. This is that the part that generates the data, right? So usually pre-processing and training need access to all the data, right to do their parts And then when you're evaluating the model, perhaps you only need a subset of data that you didn't use to train on So you can test that they about the validity of the model and finally, let's say you are serving this model You want to deploy it, right? And then what what you're deploying is just a resulting model that might be way smaller And usually people take this one They bundle them on a container or virtual machine deploy to production and use it But then if you already like have this very nice pipeline It's very important that you keep it continuously running so that you can continuously be training new models in a very easy way If you have more data, you just want to crank a lever and get a new model train So more importantly once the data once the model is starting serving You want to start acquiring more data and then be able to say, okay I want to acquire more data and perhaps re-evaluate my model and see if it's working or not And if it's not working for the newer data, let me trigger a retrain So some of the scenarios where this could happen imagine that let's say you train a model To do some character recognition and let's say beginning of time people only did zero some once So you train the best model out there to do zero recognition People will do it like this balls and they will get them a zero They will do us a line and they will get a one But then in the future, let's say some new trained the users They they decided to start using a new fashionable digit. They call To and your model doesn't really recognize it because it was not originally trained on it Right, so you will do some faulty recognition of picking this as a zero So this is where it's very important that you build your machine learning pipelines in a in a mechanism That's continuously easy to just Again press a button and have them rerun the whole pipeline to train a new model against the newer either label data or unsupervised data that you're using for your algorithms and Train it evaluate it and deploy it all in a simple fashion, right? now if you see All of these boxes to some extent they're becoming components So and this is very important to look at to look at each stage of a pipeline as a reusable component Because now if this is where you know, maybe extracting the data will act the same for you every single time But per se preprocessing data can be improved over time Let's say now we have some better techniques to preprocess the data We made some decisions on how to do that Let's say we had a first version of my training algorithm and then we we build a new version a third version, right? so Looking at this as as reusable components is quite important so that you can see the evolution of the pipeline and then Not a whole it doesn't need that mean that the necessarily that the whole pipeline needs to be changed every time set Something that needs to be changed about it You can just simply change the component that you need to update right or improve the component Moreover, if your pipeline is already looking like this, right? You can imagine. Yeah, perhaps I did this Initially with TensorFlow Let's say and I use Python on TensorFlow go to extract preprocess train evaluate and then serve my model Right, and then let's say containerize this and deploy on a Kubernetes pipeline So this could be my version one of my pipeline and it could be a very decent model right and this is actually the the approach that we're going to be exploring through the demo, but You don't necessarily need to stick to this right so you can start with this And if you're structuring your pipelines so that they are continuously They're designed to continuously be run and but then they're not actually running on the data beam They're not relying on the data being present locally at all time because that that's kind of like the this the The learning of the story Right, so it's you don't want you don't want the extract to be like downloading all the data Right from the source and then putting in the file and then having to have that file Copied open from hard disk drive to the next container on the hard disk drive to preprocess it and then having another file And then copy it you want to do something that glues these together, right and moreover perhaps this is not the right tools at Over time you want to move into more modern solutions Perhaps you say well my extraction now I'll do with Reno my preprocessing I'll do with spark training tensorflow Evaluating the tensorflow and then I'm gonna use case surf to serve this So when you start looking at your machine learning pipelines as modular components That you can actually start improving over time it unblocks a world of functionality over time and What the point I was also trying to make is how to glue this right? So the the right way is where me objects touch can be thrown in the mix, right so that you know Data extraction is reading out of your data lake That's your objects your object storage and then putting it the extraction results into object storage again Then the preprocessor Stage of the pipeline comes in it reads that file, right that are straight from obvious storage Crankset puts it back into object storage then comes tensorflow trains it reads straight from object storage, right? It doesn't need to load the data entirely if if for example in tensorflow We have the the concept of estimator. So as training is happening on top of the batches tensorflow will load the data that it needs straight from the Object store right same goes for evaluation and then the resulting model can also be pushed to object storage and we can then have a Single container for serving that's just reading a different model, right? And then that's how we do a quickly deployment and then let's say for ingesting more data You you can bring any application of your choice again throwing more data into into the object store So that if you reach a certain threshold, let's say you you have someone supervised Anomaly detection algorithm that you're capturing. I don't know a lot of HP access logs to Kafka And at some point you say well every time I get every time every after every single day or every X amount of hours I want my algorithm to retrain automatically, right? So then that's where after Kafka has thrown enough data into object store Object store come either notify you be a lambda notification or you can just pull a lever and then start the whole thing together, right? This is the the importance of actually designing your machine learning data pipelines against your object store so that you can Build these highly reusable pipelines that can involve over time and become more sophisticated, right and deliver higher value and again Make everything more useful, right now most most of the Concerns here is like, okay, how is this gonna look right? So how am I gonna develop these sort of pipelines? So For example, here if I was the developing our own Tens of flow, right? So let's say we will train that on top of image net for example you will Structure your pipeline so that at every part of the pipeline you are reading reading, right? So and then passing on to the next stage the next stage will also read straight from object store and then so far And so I can support right so that's that's pretty much how you want to go about designing in this case the pipeline that we're Gonna be walking through the demo. It's gonna we're gonna train something smaller. We're gonna train sentiment analysis That's the the hello world of machine learning. I will say But we're gonna be training that and then what you want is to be able to Do one one thing at a time, right? So One stage will pull the data Pre-process it put it back in object store next stage and then next stage next stage, right? So and this is something that most modern machine learning frameworks can support to do they like all modern Popular machine learning framework support object store. So you can do this in a very easy way That's not disruptive to your workflow and even better, right? So maybe your engineer will be concerned Okay, maybe I don't want to be uploading and downloading all the data Every time I'm making a test that they will still probably push back into like I want to have the data locally but Some really nice feature that you can use like if you should you choose to use me now for your object store is You can run me now your locally, right? So you can also use it for development and you can trust that the same API will be available when you run me now production and your pipeline on production So that that that way your engine your machine learning engineers or data scientists They can actually iterate and build their algorithms entirely around object store, right? They don't have to wait for they don't have to throw that problem for someone else to scale it. They're they're building these nice Let's put it this nice culture about around how they should build machine learning right because perhaps to some extent at some point They'll be like someone's gonna already take care like there's already some stages for pre-processing data Which I don't need to care about like I'm gonna start after those guys preprocess the data And then I'll do something different so perhaps a new model some new prediction So it's it's a philosophy, right? So this is what is also known as machine learning operations and it's the the machine learning operations It's the philosophy that as you are designed you're designing everything around components Reusable components so that people can mix and match and then improve independent parts of the pipeline as it goes, right? so With that set, let's jump into a demonstration of this Let's see how you could actually achieve this using tensorflow for it for the time being you don't have to do everything Around tensorflow like I'm suggesting if you were following along and doing this on a spark pipeline Let's say you could pretty much achieve the same result But the point to take home is pretty much the simplicity and then the next thing We're gonna do after like reviewing some simple design, right? So it's okay. How do I make these into? simple How do I make this into a pipeline and then run it in some in some orchestrator? So traditionally and I showed you in the part where you could just run this on top of Kubernetes Well, perhaps you want to pick an orchestrator like airflow or q-flow, right? So something that makes it super trivial so that you can upload your pipelines and have them rerun and rerun and Revaluate what is this part of and it's even easier for debugging because now when you're when you're looking at your pipelines they may be They may be fading out a particular point and that's something we'll see but okay. Let's jump into it, right? So for this part I'm gonna jump into my IDE and to my notebook, right? So here I have actually a like super nice IDE and What I'm gonna be you don't have to read through all of this notebook Well, this is usually how I go about designing my my machinery in algorithms, right? And by the way all the source code that I'm showing on this video It's available on github and we you can also find it on the blog post We have a blog post for me now particularly for tensorflow So you can just look me now Tenserflow right and then you'll find my hyperscale machine learning with me now on tensorflow blog post and you can follow along Now in this case as I'm designing my machine algorithm I want to be able to do stage by stage what I what I need, right? So perhaps I want to download my data put into object store and then take it from there, right? So so what I want to do on this In this is pretty much go along with the algorithm, right? So this is how I will pretty much go about making my algorithm. I will install importing my dependencies setting some base Parameters in this case. I'm gonna be pushing everything To my object store, right? So I'll be Pre-processing some data into some bucket using the mini SDK to actually download the data that I'll be like extracting and pre-processing Right, so that's gonna be the first part of my pipeline, which is let's pre-process some data, right? So in this case, I got the ACL and IMDb data set That's a data set of pre-labeled data that has movie reviews And some of the movie reviews are actually positive and some of them are negative, right? So I'm gonna download that data put into object store, right? And this is what it will come to expect and all of this part. We're gonna Pretty much skim through because there's not not much we can do but pretty much Like a pre-processing pipeline, right? This is where you could actually Be naive in building tariff around Python, right? So for example in this case, I'm being naive and just downloading the data crunching it and returning it, right? In real life, you probably want to build it this on top of like, I don't know spark So that if your data set is actually insanely large, you can actually distribute the preprocessing around spark workers So now I'm just gonna go ahead and actually preprocess my data shuffle it and then go around to Upload it, right? So now for the next part, I'm gonna be taking these datasets. I'm gonna be encoding them Using a layer and existing a pre-processing layer I'm gonna be leveraging Google's universal sentence encoder model, right? This is because I'm building a deep learning model So something really nice about deep learning models is that you can stack the layers, right? And leverage an existing model. So now after I have downloaded this, I'm just pretty pretty much I want to download it from TensorFlow Hub and put it into object store. Why would I do that? Why not have the pipeline downloads this model every single time? Just as I say it, you should probably be noticing like where you're running these pipelines and retraining You don't want to be re-downloading from the internet stuff that you can pretty much cache in your object store layer And then have the pipelines pull at very high speed from it, right? Because that's running on top of your own infrastructure So you don't need to be pulling things over the internet And even if you are developing things, right? As an individual engineer And let's say you're collaborating on a local lab If the data is everything is on the local object store, things are gonna just go faster So after downloading the data and putting it into object store That's what I'm doing on the first pipeline part I'm just gonna download these things and then just load the model So now it's pretty much a simple and then load the model, right? So far everything is still highly compatible with working with things locally So we'll start seeing the magic after we're done post-processing the data In this case I'm just gonna download, have some functions to serialize my samples Extract them and put them And now we're gonna take some time, right? So this is where the part where some start pre-processing Of course it's something that takes time, right? Especially if you're working with the whole dataset So what we're gonna do here is through the magic of editing We're gonna jump to whenever that's done Man, I've always wanted to do this All right, that took some time And after we're done pre-processing our data Of course, now you want to get in the business of training, right? So this is where the things really start making sense about You don't have to load the whole data Because for example, particularly here on TensorFlow I can start loading my... Right now I'm listing straight from Obiex Storage getting some list of files To configure TensorFlow properly to read the data I just need to set some embedded variables Which is something we set at the top And then all the files I'll be preparing them And this is where the source comes, right? So as I'm giving TensorFlow the data to read I'm telling it, read it straight from Obiex Storage I'm not actually telling it to read from a TMP file or anything So that as my machine learning algorithm goes on trains In the particular case of TensorFlow We'll see the estimator loading the data as it's needed This is also a very common pattern access Like, let's say you were doing Spark You wouldn't be downloading the whole dataset on every work As you're crunching data Spark will only be pulling the data that it needs to actually be crunching So have I run these stages? Yeah, I have not run these stages The nice thing about preparing your data in this set Is that, for example, this is the model that I'll be training So it's going to be a pretty simple dense layer It's a couple of dense layers And then just applying an activation function on top of that How many weights have not printed the weights? It's going to be like 135,000 weights to train So it's not a very deep model Let's say that this way So now because of my data All my data is already on a TensorFlow dataset And the TensorFlow dataset That's really strange for ObjectStore And I even preprocess my data into a format that's friendly to TensorFlow So that as the data needs to be ready to process It can just be read straight from ObjectStore And then each file has a bunch of records to process Like a whole batch, that's how I decided to preprocess my data around I'll just pass this around to TensorFlow I'm also going to tell TensorFlow Let's put my checkpoints on ObjectStore And this is very important because as these trains Let's say the training is going to take a few hours or a few days You want to be able to check the progress And perhaps you want to start TensorFlow on top of that TensorFlow is a great tool for TensorFlow users That they can see how training is going Even if it's not done You can just go checkpoints And see what's going on over there And then I'm just going to throw the log directory for that And here's the second part, model feed So I'll just say let's train this for 10 cycles 10 epochs So we start training And then again, this is going to take some time So through the magic of anything, we'll skip to the end Ready? All right, training is done So we run our data to train epochs And now after this part is complete The rest of the drill, you already know it So pretty much we will save this model straight to ObjectStore I'm going to ask how did the model did to my training So we can see that part And then I'm going to run some evaluation About how good is this model performing You get some data that I set apart from the beginning And this data is also stored on ObjectStore Therefore, these map data sets that I'm actually running straight from ObjectStore They are very fed into the TensorFlow And TensorFlow can tell me, okay, so you have an accuracy of 85% Not state of the art But I mean, I try to keep these trainings small But you get the idea, right? And I'm not trying to go too much into detail about here Because pretty much I want to take you through the normal cycle Of what machine learning engineer will do And the last part is, of course, loading straight from TensorFlow To show me the throughput or the performance of my algorithm I could, of course, do some predictions Here I have some sentences, some of them are mean Movie reviews, so of course, being able to say Yeah, this was extremely good, I loved it It's a positive and then redacting negative, right? So you see some false positives over here So you get the idea, right? So the model is trained, it works, 85% of the time So now we want to serve it So serving is also something that can go straight to object store So in this case, I'm going to review my model using a CLI tool Actually, this step was not needed for this video But I can start, let's say, a TensorFlow model server There's TensorFlow service, a technology for serving TensorFlow models There's a key value in other technologies But once I start this, I'm going to start it on a Docker container Load some Python requests and then just make requests to this guy And be like, okay, show me some predictions Right, so I love this movie And there we go So I actually sent all my same samples through a JSON interface And I got what I was expecting So you can see how I'm gluing everything to object store So that when this actually reaches production, everything is ready So now, traditionally, as a data scientist, I'll be saying, done Let's take this to production I could just throw all this Jupyter notebook into a large Python file And of course, everything is ready for prime time Just put this in a container, I will tell my DevOps, run it And then things will work But of course, we've seen what is the problem with this approach It's not a highly scalable approach And the main idea that you want, the main takeaway is Okay, we want to improve on top of this, right? Now, the best part that if I design my whole pipeline Around very well-defined stages And very on purpose, I put everything into a single large file I didn't try to, I could have organized this very nicely To separate files, you can do that I actually encouraged you to do that For the sake of this video, I actually made a very large file So that you could actually see that, for example My orchestrator of choice is Qflow for this video as well If I want to transition this to a pipeline on Qflow It's going to be dead simple, right? So I can use actually, for example, Qflow pipeline components Python functional components on Qflow And I can just take my chunks of Python code Wrapping on a stage In this case, my preprocessed data function Is going to be one of my stages, right? The other stage is going to be my training stage, right? All the code that you saw me run for training My evaluation code My deployment code, right? All of that are going to be stages of my pipeline And Qflow makes it very simple through the DSL Kind of like, the pipeline SDK has a DSL For actually designing pipelines this way And that's what I'm doing, I'm just taking my code And actually, I don't want to bother you With just me copying data from left to right That's why I prepare this pipeline But the main takeaway is that Okay, the code is already ready To read data out of a different location In this case, object store So there's no need for me to set up anything To copy data into the container, right? So I don't need to set up anything Even better, I can just hit run on this pipeline On the right side, that will beam it up to my Qflow instance Let's do that right now Actually, I think I configured this to To compile, I want to run There we go, let's run this straight Let's not waste any time Right, so let's go to my Qflow instance Which I'm running here I'm going to log into my Qflow instance And nothing's going to load There we go That's the danger of live demos Even with editing, I wouldn't tempt it So let's run to run We'll see my sentiment analysis pipeline Actually running, and you can see that All the things that we were doing And running it through my computer This Qflow is already running on a machine With GPUs, so it's definitely going Way faster than what my laptop did Right, so the preprocessing, the training The evaluation, let's pick up the evaluation Right, so this is going to throw me A file to open So let me bring an editor For that So after opening this file I can see the accuracy training Right, so because of random initialization I was fixing a very particular Random number, I got like better accuracy On my machine Actually, no, this is actually The same accuracy I got on my notebook But you get the idea Right, so what You get the idea, right, so when I was essentially running things before Let's see if this one opens, yeah, this one opens in browser So everything that I was running before On my notebook, because I Particularly designed it to Read the data straight from object store Right, I was designing and developing Everything running locally, right So I had a minio running locally Copying the data inside everything Reading straight from that place Preprocessing, putting it back Training straight from it Semi-checkpoints, doing evaluation Against it, and then finally Serving the model straight from object store It was that simple that at the end In this case, Qflow, and made it Run as a machine learning pipeline Now, every time I need to return my Pipeline, I can just rest assured that I can just Pull the lever and program Now, a new machine learning model Will be born, right My last stage actually is leveraging some of the nice Features from Qflow, for example Serving models My running on HTTPS Where's my model server? There we go Yes, nothing's going right This demo Here, right, so now I can do TensorFlow Serving natively from Qflow So my pipeline can actually take that upon itself So that when it's done If my evaluation goes right And I pass it on threshold, perhaps That's when I move to deploy the model And that's about it, right So machine learning operations It's a development philosophy That's what I've been trying to The only thing you want to take from this talk Is that Machine learning operations should be a Development philosophy You want people to be like, okay I'm going to be a better preprocessing Or I'm going to be a better Extrater stage, right But then they build it in a way That can be reused by someone else Or they can also rebuild their own Machine learning algorithms on top of their own Reusable components That's what you want your organization to move The machine learning pilots are highly reusable They can be highly auditable as well So that's one thing The other one is Machine learning models may go stale, right So that's why it's so important that you Keep retraining with newer data Whether it's newer labeled data Or newer unsupervised data Because business cases Will change Your business will evolve over time And your models need to evolve with it We saw that sample of the MNIS optical character recognition Example that I gave you Where people may come with new digits And then your model needs to retrain quickly For that as soon as you gather some data To start identifying those new use cases You want to retrain So Then storage infrastructure Is way cheaper than computer infrastructure So if you're building everything around Storage infrastructure, right Compute, you can always burst it You can go to the cloud and say I'm going to do this train and the plans Yes, GPU machine, TPU machine that I can And then when I'm done I want to turn it off That could also be part of your pipeline Starting a fancy VM somewhere Have it run and pull the data from Your data lake and then shutting it off afterwards And then the storage infrastructure Tends to be way cheaper to operate Than compute infrastructure So that's another very important Highlight of why you should Be designing your pipelines this way So we're already touching that Building all components reusable Has huge advantages, right You can have your machine learning Engineers actually collaborating Or your DevOps actually improving Parts, right, so like let's say Machine learning engineer doesn't know how to do the Trino You can get someone to do the Trino for them, right So someone says, well We can crank this faster through us In a Spark cluster and we already have one We can do that for pre-processing data, etc You get the core idea And lastly Yeah Machine learning models need to Be conditionally training or They're going to go stale, right And you don't want to do that So that's been all for today Thank you for attending this talk My name is Daniel Valdivia again Please follow us on Twitter Hit us up on github, right Or join our public Slack If you want to hang out with the community If you have any questions about How to run your own object store You can ask us there Or you can just visit our website And thanks for your time And hope you have a great time Remember the source code Is going to be available on github And it should be on the description of this video Alright, see you guys