Welcome to Understanding Energy Efficiency in the NCC. The focus of this presentation is on the fundamentals of energy efficiency and how the NCC handles energy efficiency requirements across the volumes. This is what you will learn about in this presentation. What energy efficiency means, why energy efficiency is important, key energy efficiency concepts and terminology, how to achieve energy efficiency, energy efficiency requirements in the NCC, other useful resources. What do we mean when we talk about building energy efficiency? Energy efficiency means using less energy to achieve the same outcomes. The ratio between energy used compared to the energy service actually provided and is usually expressed as a percentage. In other words, energy efficiency is how efficiently the energy is used to provide the end-use service. Using energy wisely and economically to sustain everyday life, live comfortably and support well-being. Reducing the need for energy consumption, electricity, natural gas, etc. for heating, cooling and lighting. Building energy efficiency in the NCC. There is no exact agreed definition for energy efficiency. The most technical definition and source of the term relates to how much output a machine or energy service gets for the energy that is input, i.e. how much of the energy input to a machine is lost and how much is able to be used to produce an output. But the term is used more broadly now in the context of reducing energy use or conserving energy. In building terms, key sources of energy use are heating and cooling of living spaces, lighting, heating hot water, operating electronic devices. The NCC energy efficiency provisions are designed to ensure that a building is designed and constructed in such a way that it is possible to operate a building with an appropriate minimum level of energy use given its purpose and geographical location. There are performance requirements for both class 2-9 buildings, commercial and class 1 and 10 buildings, domestic. Why is energy efficiency important? In recent years, awareness has increased about increasing levels of greenhouse gas or GHG emissions and the effect these emissions may have on global warming. While greenhouse gases are a natural part of the Earth's atmosphere to capture the sun's warmth, greatly increasing the amount of GHG in the atmosphere increases this natural capture of heat, resulting in the warming the planet is seeing. While the building sector is not the largest contributor to greenhouse gas emissions, it is one of the fastest growing sources. Energy use in buildings account for around 20% of all energy-related greenhouse gas emissions in Australia. The NCC aims to contribute to reducing GHGs by increasing the energy efficiency of Australia's building stock. Energy efficiency reduces pressure on bills in the short term because households and businesses pay for less electricity while the equipment maintains the same level of service. For example, efficient fridges can have the same features and storage capacity as minimum standard fridges but will cost less to run. Energy efficiency also reduces pressure on prices in the future because it reduces the need to expand the electricity supply system to cope with increased demand. This means that all energy users can benefit from energy efficiency actions. The Australian Energy Market Operator, AEMO, lists increased adoption of energy efficiency measures as one of the factors in the recent reduction of energy demand. Key energy efficiency concepts 1. The definition of these terms in the NCC are specific to the NCC. It is important to check the definition and make sure you understand it. Don't rely on their everyday understanding of these terms. Some energy efficiency terms are defined differently for NCC Volume 1 and NCC Volume 2. For example, the definitions of condition space, building envelope, glazing, climate zones. The map is Figure 2 from Schedule 1 definitions in any volume of the NCC. The map, together with its associated table, defines the eight NCC climate zones for energy efficiency. There is a larger, expandable version of this climate zone map available at the ABCB website www.abcb.gov.au along with state and territory maps. The different climate zones are shown by the different colours. Climate zone 1 is the dark orange colour in northern Australia. Climate zone 8 is the white parts in the southeast of the country. The eight NCC climate zones are described as climate zone 1. High humidity summer, warm winter. Climate zone 2, warm humid summer, mild winter. Climate zone 3, hot dry summer, warm winter. Climate zone 4, hot dry summer, cool winter. Climate zone 5, warm temperate. Climate zone 6, mild temperate. Climate zone 7, cool temperate. Climate zone 8, alpine. Appropriate design for the climate is key to achieving energy efficiency in a building. For example, in climate zone 1, buildings should be designed to reduce heat gain from the sun so you want to minimise how much sun enters windows and how much heat the windows will transmit into and out of the building. Ventilation is important for cooling the building naturally. In climate zone 4, you want to minimise sun in summer and ensure adequate ventilation. But in winter when it is cooler, you will usually want to maximise the amount of sun you get into the building in order to warm it naturally. In climate zone 8, where it tends to be cooler all year and very cold in winter, your main aim is to minimise the loss of warmth from inside the building. Conditioned vs non-conditioned space. The explanation for conditioned space on the screen is simplified a little for space reasons. And because the purpose of this module is not to present a detailed technical analysis of energy efficiency requirements, it is sufficient in this module for the trainees to understand that some spaces in a building are conditioned and some are not. The definition varies between volumes 1 and 2. The trainees should look at the definition in the relevant volume before making any decisions based on the classification of a space as conditioned or non-conditioned. The NCC doesn't define non-conditioned space, but it is logically the opposite of a conditioned space. Since the definition of conditioned space varies a little between the volumes, so does what would be considered a non-conditioned space. Check first before making any assumptions. This classification is important because a non-conditioned space will impact on the temperature in an adjacent conditioned space if there is a temperature difference between them. So for example, a warm conditioned space that has non-conditioned spaces on three sides will lose heat to all three of those non-conditioned spaces. If a cool conditioned space has a warm non-conditioned space on one side, for example the western outside of a building in summer, then the conditioned space will gain heat from the non-conditioned space. This heat loss gain continues between any spaces or surfaces with different temperatures, essentially until the two spaces' surfaces have the same temperature. Building Fabric Envelope The envelope of a building can be thought of as identifying the boundary between a conditioned space and a non-conditioned space. While the building envelope is always formed from parts of the building fabric, some parts of the building fabric may not be part of the building envelope, for example internal walls which separate condition spaces, i.e. rooms from each other. The materials used in the building fabric and the design, construction and integrity of the building envelope help to make the building more energy efficient. They can help to reduce the transfer of heat into and out of the building. This helps to maintain a comfortable temperature in the conditioned spaces and reduces the need to use energy to heat and or cool the building. Using appropriate materials in the building fabric, along with appropriate insulation, can help to limit unwanted heat flow and reduce the amount of energy needed to heat or cool the conditioned spaces in the building. If you identify the areas of the building that are conditioned or can be conditioned, or for volume 1, constitute a habitable room as defined in volume 1, which is likely to be conditioned if it becomes uncomfortable. The building envelope is made up of the walls, roof, ceilings and floors that form the boundary of these conditioned spaces if there is a non-conditioned space on the other side. Heating and cooling loads A building's heating and cooling loads are calculated using approved software. These calculations are based on the building's size, design, climate zone, orientation, ventilation and the materials used in the building envelope along with a few other factors such as any shading from other buildings or significant trees and the colours used in external materials. Only approved software can be used to calculate heating and cooling loads and this software is different for residential buildings versus commercial buildings and to some extent between different states and territories. A building's heating and cooling loads are the key factors that determine a building's star rating under the various energy efficiency rating schemes. Only NATAS software allocates a star rating to buildings. Software approved for use in H6V2, verification using a reference building, is unlikely to allocate a star rating. The classification of conditioned space in a building makes a difference to the calculation of heating and cooling loads and therefore the building's energy efficiency star rating. Acceptable heating and cooling loads for buildings are different in different climate zones. This is an acknowledgement that it is easier to minimise energy use for heating and cooling in some climates than in other, more extreme climates. For example, it would be difficult to design a building that had no requirement at all for mechanical heating in an alpine area. To require a building in climate zone 8 to have the same heating and cooling load as a similar sized building in climate zone 1 or 4 would not be reasonable. Heating and cooling loads and the software used to determine them are discussed in more detail later in the presentation and in the specific modules that discuss the energy efficiency provisions in volume 1 and NCC volume 2 using the energy efficiency provisions in NCC volume 1 and using the energy efficiency provisions in NCC volume 2. Using the NCC climate zones. Question 1 Which states or territories have climate zone 8? Alpine areas. New South Wales. Victoria. Tasmania. Question 2 Are Adelaide and Perth in the same or different climate zones? Both capital cities are in climate zone 5. Warm temperate. Question 3 Exmouth and Dampier in Western Australia and Mackay and Rockhampton in Queensland are of similar latitudes. Are they in the same climate zone? No. Exmouth and Dampier. Climate zone 1. High humidity summer. Warm winter. Mackay and Rockhampton. Climate zone 2. Warm humid summer and mild winter. Complete the sentences below. A building's watt represents the amount of energy required to maintain a comfortable temperature in that building. The answer is heating, cooling load. A building's structural elements such as the walls, roof, ceilings, glazing and floors are referred to as the watt. The answer is building fabric. A watt in a house is a space that is heated or cooled to maintain a comfortable temperature. The answer is condition space. Any space that does not have any services to heat, cool or otherwise alter the temperature of the air is described as watt. The answer is an unconditioned space. What term is used to describe the parts of a building that separate a condition space from non-conditioned spaces including the outside of the building, the building envelope? The design, materials and construction of a building's envelope help to prevent unwanted transfer of heat into and out of the building. This helps to keep the living spaces at a comfortable temperature while minimizing energy use. The building fabric is all the structural elements and other components used to make a building which includes the roof, ceiling, walls, glazing and floors. The parts of the building fabric that separate condition from non-conditioned spaces belong to the building envelope. Not all parts of the building fabric will separate conditioned and non-conditioned spaces. Some will separate non-conditioned spaces from each other or they will separate conditioned spaces, e.g. internal walls. Key energy efficiency concepts too. In terms of insulating properties and energy efficiency a high R value is usually good but whether you want low or high solar heat gain coefficient SHGC and U values depends on how you want the particular window to function. So in a northern location where you want to keep the sun out of the building all year round a window system with a low SHGC and a low U value is preferred. But in a southern location where you might want to allow the sun into the building in winter but exclude it in summer a high SHGC and a moderate U value might work better. Note that the glazing energy efficiency requirements in the NCC apply to windows in the building envelope only. So a window in an internal wall does not have to meet these performance requirements. For example, a window placed high on the wall between a hallway and a study to let in more light. Note that this window might have to meet other performance requirements e.g. to ensure safety and minimise noise disturbance. Glazing. In the NCC the term glazing includes the glass and the frame associated with it in the building envelope. The term is defined slightly differently between NCC volumes one and two. It can apply to windows and doors in walls and some roof windows. R value. Total R value. R value is a measure of how well a material or building element insulates. That is how well it resists the transfer of heat from one side to the other side. It is a measure of the material's thermal resistance. R value is a defined term in the NCC. The formal definition is the thermal resistance of a component calculated by dividing its thickness by its thermal conductivity. The higher the R value, the better material is it insulating. That is, the less easily it transmits heat. The NCC also defines the total R value of a building component or assembly. This is a calculation of the sum of the R values of the individual component layers in a composite material or assembly. The calculation includes any building material, insulating material, air spaces between the different layers, and associated surface resistances. If you think of a pre-made exterior insulation and finish wall panel, which could be assembled from four or five different layers, e.g. a metal outer layer, a foil moisture lining, an internal frame, an insulation layer, and an inner shell, each of these layers will provide some degree of thermal resistance. The total R value is the sum of all these plus the thermal resistance of any air gaps. Builders might not often have to calculate an R value, as these values are usually included in product specifications. You will usually see R values quoted for the building materials typically used in walls, floors, ceilings, and roofs. You might need to check whether the value quoted is a total R value or just the R value of an element. You might also need to calculate an R value if you are using an unusual building material or method. For the purposes of calculating heating and cooling loads and therefore energy efficiency star ratings, it is generally the total R value of a wall, roof, ceiling, or floor that matters.  U value is a measure of how easily a material or building element transmits heat from one side to the other. It is a measure of the material's thermal conductance. The higher the U value, the more easily the material transmits heat. The lower the U value, the better the material is resisting the transfer of heat. That is, the better it insulates. A material's U value is the inverse of its R value. U values are calculated for any type of windows, including those in walls and roofs and ceilings. The NCC defines the total system U value of a building component or assembly. This is a calculation of the sum of the U values of the individual component layers in a composite material or assembly. The calculation includes any glazing, the frame, any insulating material in the frame, thermal breaks or air spaces between the inside and outside, any gases used, e.g. argon between double-glazed windows, and associated surface resistances. The definition of total system U value varies slightly between NCC volumes 1 and 2. You will usually see U values quoted for glazing in window systems.  SHGC, Total Solar Heat Gain Coefficient SHGC and Total SHGC are defined terms in the NCC. SHGC is a measure of the proportion of solar energy or radiation that passes through a glazing system, which includes the glass and the frame. In other words, it measures the solar heat gain through the glazing system. It is a measure of the glazing's solar transmittance. It is expressed as a number between 0 and 1. A high SHGC means that the glazing system allows for more solar energy heat into a room, i.e. a value closer to 1. A low SHGC means that the glazing system allows less solar energy heat into a room, i.e. a value closer to 0. A low SHGC is good in some circumstances and a higher one is better in other circumstances, depending on how the glazing needs to function in the building. The definition of Total SHGC varies slightly between NCC volumes 1 and 2. Like the U value, you will usually see SHGC values quoted for glazing in window systems. You will need to understand whether the value quoted is the value of the glass or frame or a Total SHGC for the entire window system glazing. Match the term to its explanation. A transparent, translucent material and its frame in the external fabric of a building is glazing. A measure of how easily a building element conducts heat is U value. A measure of the amount of heat transmitted through a window, glazing door or skylight is SHGC. Solar heat gain coefficient. A measure of how well a building element resists the transfer of heat is R value. How do we achieve energy efficiency? Optimal orientation is key. A building that is well positioned on the block to catch winter sunshine and reduce summer sunshine, for example, in southern latitudes, will be much more comfortable to live in than one that is poorly positioned and will need far less mechanical heating and cooling. This is one of the simplest and most effective ways of obtaining good energy efficiency, although it is not enough on its own. The materials used in the building fabric also play a role. In warmer climates, lightweight materials that reduce thermal mass can be effective. However, in cooler climates, materials with more thermal mass are usually preferred. Glazing. That is appropriate to the climate. Orientation and position is also important. This involves considering more than just the type of glass and frame. It extends to the size of the glazing windows in different walls. In southern latitudes, large windows on the north that get winter sun can help to warm the building during the day and at night if there is sufficient thermal mass in the house. In the same situation, windows on the southern side of the building should ideally be kept smaller as they act as a source of heat loss, regardless of the type of window used. Insulation is also key to reduce loss of heat in cooler weather and heat gain in warmer weather. Insulation can be in the ceiling, under the roof, under and at the sides of the floor and in the walls. In other words, throughout the building envelope. Ventilation is another key consideration that is often forgotten. For example, in warmer climates and in summer in most parts of Australia, good ventilation can help to clear the heat that builds up during the day and provide a comfortable temperature for sleeping at night. In climate zones that are warm all year round, e.g. climate zones 1 and 2, good ventilation can make a big difference to how much heat builds up inside the building and to users' perceptions of the thermal comfort in the building. Shading can also be useful to consider. For example, in cooler climates, positioning the building to avoid overshadowing by neighbouring buildings in winter and planting trees or using features such as pagolas, eaves or wing walls to reduce sun exposure and heat gain on the eastern and western sides of the building. In northern climates, where the sun can enter the building from all sides through much of the year, wider than usual eaves can help to provide shade on all sides and minimise solar heat gain. Energy-efficient building systems and appliances also play a key role. A more efficient heating system can save significantly on the energy required to heat a building when mechanical heating was required. The key mechanical systems that contribute to energy use are heating and cooling, lighting, heating hot water, pumps and other equipment related to pools and spas. Note that small appliances are also big users of energy, particularly as the number and size of devices in the typical home or workplace continues to increase and they tend to use more and more standby power. But as they can vary, i.e. they are not a permanent fixture within the building, they are not included in assessment of energy efficiency for the purposes of building approval. However, all efforts to minimise heat loss gain are potentially wasted if the building isn't well sealed. Since all the warmth inside the building, in winter, will escape, an unwanted heat will enter the building in summer. Good building sealing means that if you use energy to heat your building, that warmth will be retained for longer inside the building and so the building won't require so much mechanical heating ditto-recooling the building. All these factors listed and others have to be considered when it is cheaper and easier to ensure that the building will function well for the climate zone. A building is approved to be built as designed which includes all the energy efficiency features in the approved plans. This then must be carried through in construction, i.e. the building must be built as approved and you shouldn't be changing features that contribute to the energy efficiency of a building. For example, reducing the insulation in walls. Installing sealing installation poorly or choosing a different material for walls or glazing can greatly change the actual energy efficiency of the building. Example, achieving energy efficiency. How does the concrete floor slab contribute to energy efficiency? What kinds of materials would you use in the building envelope? What sort of R value should these materials have? What kind of SHGC and U values would be best for the glazing in this building? What purpose do the eaves on the building serve? Why is the southern window designed to be smaller than the northern window? If you rotated this building by 90 degrees so that the living areas faced east west what could be the impact on the building's energy efficiency? It is important that the design must suit the climate to respond to the orientation. Example, achieving energy efficiency. How is the design of this building different from the design of the Canberra building? Why is the lightweight wooden floor used in this building? What kinds of material could you use in the rest of the building envelope? What sort of R value should these materials have? What kind of SHGC and U values would you select for the glazing? Why are the northern and southern windows the same size in this building? In what different ways can the windows contribute to the building's energy efficiency? It is important that the design must suit the climate and respond to the orientation. Energy efficiency in the NCC Building energy efficiency is largely assessed by rating likely performance of the building as designed against the standard. A building can demonstrate compliance with the majority of the NCC energy efficiency performance requirements by getting a rating against nominated energy efficiency standard. Some performance requirements require other forms of assessment, evidence. The standards that can be used to rate buildings are different for commercial and non-commercial domestic buildings. There are also some differences between the standards that are acceptable in different states or territories. Both volumes 1 and 2 contain performance requirements that focus on the performance of the building fabric which means the extent to which the building fabric allows for heat flows in and out of the building. The energy efficiency of the key fixed building services such as air conditioning systems, lighting and hot water generation. Volume 1 also includes requirements for the energy efficiency of swimming pool or spa plant and provisions to allow for monitoring of energy usage. Volume 2 includes requirements for adequate ceiling of the building envelope. These inclusions reflect the key concepts and approaches discussed earlier in this module as making a difference to building energy efficiency. Some of the provisions in volume 3 also relate to energy efficiency. In particular, the performance requirements for heated water services require consideration of the amount of energy used to heat the water and the greenhouse gas emissions that are released. Other resources available ABCB has developed a range of calculators to assist practitioners to develop energy efficient building solutions. Use of these calculators may be helpful, but it is not mandatory, i.e. you do not have to use them and they are not compliance tools or verification methods. For example, you can use an ABCB glazing calculator to identify or test glazing combinations but you should not use the results of the calculator as the sole evidence that the glazing chosen for a building is appropriate and meets the performance requirements. There are videos on how to use some calculators. There are handbooks specifically on applying the energy efficiency provisions of each of volumes 1 and 2. There are many different case studies that cover a wide variety of topics true or false. The energy efficiency provisions are the same across volumes 1 and 2 of the NCC. If you chose false, yes that's correct. The energy efficiency provisions are different in the two volumes. Differences include the definitions of some energy efficiency terms, the performance requirements and the DTS provisions. As the types of buildings covered by each volume are different so are the performance requirements and DTS provisions. Some definitions also naturally change. Which volume or volumes contain provisions designed to improve building energy efficiency? Is it NCC volume 1 NCC volume 3 volumes 1 and 2 or all volumes? The correct answer is all volumes. Volumes 1 and 2 contain provisions for ensuring minimum energy efficiency levels in different types of buildings. Volume 3 contains provisions that relate to the use of energy for heating water in any type of building. Key points. Energy efficiency provisions in the NCC aim to reduce the energy required to operate a building and therefore the GHG produced by buildings in Australia. Focus on the building envelope to minimise the use of mechanical heating and cooling to maintain a comfortable temperature of the building. This module encourages efficient use of energy to operate a building's services including heated water services, use of low GHG energy sources, different requirements for buildings in different climate zones and of different building classifications. Thank you for your time. That brings our presentation on understanding energy efficiency in the NCC to a close. If you'd like more information please visit abcb.gov.au


What's up everybody, Jason here for jazbyzcasebrakes.com 2021 hip raid, autograph, baseball, sapphire, hobby box, series one, four box break Number two random letters Stop yelling Hollywood So again guys, I've actually didn't see this break last time with Joe, but these baseballs do look very cool. There's like crystal Again, there's leather crystal hand painted 24 karat gold infused baseballs in every box Harder to find these baseballs. And of course the one-on-one hand painted baseballs are pretty fun And something you won't find in any other product and then of course 18 total spots everybody gets a random letter from the list below We go by players first letter of his or her last name that appears on the card or baseball Of course, so for example, we pull Ernie Banks It'll go to the B spot for banks if we pull Cal Ripken Jr It'll go to the R spot for Ripken and if we pull Ichiro just straight up Ichiro Then it goes to I Spot number any letter not represented on the list will be randomized to the group if there are any multiple hits And if there's any multiple baseballs that are not on the list, they'll be separated out not randomized as one lot So there you go dice roller Pat Wolfe Is that is that really that funny? I mean that's trills All right Pat down to Nicholas. You got to start You got to stop hammering that caps lock too much man All right, so Pat Wolfe at the top down to Nicholas and then you have a through the combo spot down there at the bottom dice roll Five into two seven times Good luck. One Two three Four Ryan Carlson one two three four Five E down to M. All right, so Jamie with the e-spot Steve with the b-spot Peter with letter C John there with H and G Brian Graf with D Joe Pizzle with P Matt Bucca were letter L Pat Wolfe with R Nicholas with F Nick Coba with T Mike Tyler with a Michael P with the combo spot Y V I N O Right here with K Nicholas with J Brian crowd will W Matthew with S and Ryan Carlson with M. I any trade any trades guys Clean kershaw is not bad, right? What about Scott Cosmere? What about Al K line? I'm just looking at all the case Kellan egg. That's there you go Anthony K Brad Keller Mitch Keller Trevor Kelly and Kepler. Come on man. Come on Ryan I think outside the case Carter Keeboom Kikuchi Those guys could probably be in the set.  All right. Thank you guys Well, see you then. All right, Michael Robins and actually just got the last team in prism So I'll be doing that and that's most likely probably gonna be my last break and then Sean will take over for you guys for the rest of the night Well, actually Hollywood The that would go to Jesus Christ, so that is what the C spot. So unfortunately won't be able to get that God will not work. Hey, it doesn't really matter what he signed it, right? My autograph could say God Doesn't mean I'm going by the name of God All right guys. Good luck. I didn't I don't really I've never I don't really go to church and I never did anything besides getting baptized so Whatever God's real name is where it goes to All right, good luck. All right, so here are the four. Oh My gosh, it's heavy and it the Saran wrap is kind of coming off. But as you can see it's still sealed. So Slow down fly This is amazing. I actually didn't check this out last time. So 20 out of a hundred. So yes, there could be those trifecta ones where it's literally this whole same player in all three or It may just be all three different players It all just depends That's one box, baby First one Have you recognized that signature Nice little Rawlings black black and gold And it's DD Gregorius letter g Look at that. You did not get God But you got Gregorius Dude, I carried four of these over here. That's probably at least a hundred pounds Yeah, I like I hate that they didn't put the baseball rights. You can't even display it right come on, man fanatics authentication There you go DD Gregorius Probably not Hollywood unless you want to pay for the extra shipping But yes, you'd probably have to pay an extra 20 30 dollars Uh, Devin if we sell out all the spots plus fillers. Yeah, and next one another black wall It is Ozzy Albie's letter a going to Mike tower It looks like the ink is a little fading off already but letter a Mike tower And last one here Heston cursed at very nice letter k going to Look at this. You don't need to tell me that all of the people I was late I mean, I don't think I named out cursed at though because it wasn't in the MLB players list yet But look at that.  Like back into the box that holds all three Okay, so there you go guys. That's the first one. Let's go with the next one Oh, wow. Oh, actually this is Well, I guess it still goes with the same letter You guys recognize that signature before I show it big red machine inscription Big red machine. That's ken griffy senior going to letter g jonah Oh, look at this, right? You know what? No complaining no more man. No complaining I don't even I don't know joe ever took these balls out. But look at that Heston cursed at little crystal Wow, this is so sick. Can I take it out of this? Where's where's the autograph? I don't see the autograph. I was unblind Oh, it's at the bottom. I think There's authentication Sure Like I said, Hollywood, I I can't guarantee you we're even gonna allow it to be honest I can't even get to we're gonna allow it. You can try though, but I really doubt it I'll take out the baseball There you go. That's so cool There you go letter k last one louise severino letter s when a matthew hedges Next one 67 out of 100 I haven't pulled any hand-painted ones, but I know joe pulled one of those in the first break Think you pulled a wander franco, I believe Oh, look at that deon sanders. That is sweet Deon sanders letter s going to matthew hedges There we have blue ink Devin williams letter w going to brian croft Jim tommy black with gold ink jim tommy Very nice letter t going to nick koga Oh, it looks like all the authentications are also in In here too guys, but I'll just leave one in there Alrighty and the last one guys 51 out of 100 Good luck. So I got guys after this. I got to quickly do another break and then Sean will be here and he'll take over on 7 o'clock first one Very nice denis eckersley There you go letter e Jamie Let's save the middle one that only looks like a glass one And we have lee Lee smith i was trying to read it out hollow fame 2019 I have no idea who lee smith is But he's a hollow famer letter s going to matthew hedges And the last one is actually a pretty pretty popular name See if you guys can recognize this signature This little smudge because it's kind of like a little water, but Anybody recognize that signature?


So this talk is going to be a little bit different from the ones you'll probably hear I'm not going to talk about the technology I'm not going to talk about agile practices or processors or I'm not even going to mention the manifesto that much This talk is more about how other business functions work. We're all Here's a quick call of hands who here works in it in some fashion Yep, most of you that's pretty much what I expected So this talk is not going to be about it. This talk is going to be about everyone else who's not it and how you can actually work with them to Convince them and help them help you become agile agile an IT division is Not self-contained. It might be a silo. It might be a matrix organization, but you have interfaces to Finance to HR to sales and marketing to all the other different divisions and functions across the organization So we're going to talk about is how we're going to align across the organization so that you can be successful so Let's actually have a quick little bit of a question and answer here What where is your problem? Where is your pain point in your organization when you're trying to be agile? HR HR which HR policies KPIs Performance metrics. Yep. Where else roles and responsibilities. Okay. Yeah job descriptions position descriptions Who's the business? marketing sales very good, so That's a fair misconception. It happens quite often agile isn't faster agile is sooner And it's not necessarily cheaper. No So we'll talk about most of these things and I'll make specific attention to call out some of the HR issues and some of the sales and marketing issues So just for those of you who don't me. My name is Evan Laborn. I'm currently based in Singapore. Though my I was grew up in Melbourne, Australia and Sydney, Australia for the last 36 odd years. So How's the cricket going? Not not not well for you or not well for me Beautiful all right good to hear Alright, so let's actually start with the core principle of why you're in business Here's another show of hands. Think of your company's outcomes


under blue only certain bitches can pull this off so if you don't have the confidence to pull it off don't get this say hey y'all what's popping welcome back to my channel for today's video is gonna be a try on ho african mall the one and only only company african mall because we love being on a budget I love african mall because it's so affordable but it's so fly and that's what I'm about you already know what it is I like I like making the cheese stuff look expensive okay so we're just gonna get hop right into it do y'all like my hair it's cute or whatever I'm looking like poison ivy right now but that's okay um so yeah so we're just gonna get started so don't forget to like come and subscribe to this video I hope you guys enjoy it and let's get started so this is the first I'm gonna give y'all a full like body but I can't because my camera this is the full set right here I'm kind of far so I can't even see myself cute or whatever this is kind of reminding me of like a cute little sporty outfit but I'm not really liking this band because it keeps falling in girl like what are you doing you're supposed to stay there to suck all this shit in but you are like came in and exposing everything damn but anyway so this is the first thing I really like yeah I think I'm gonna wear this today so it's super cute it's like a cute sports bra looking thing and yeah came in multiple colors but I really like green cuz I like all green on me so yeah this is the second thing it's a say I'm gonna show y'all the skirt in a second so basically the concept of this set is basically like a full like t-shirt but they like cropped it and made like a skirt in the top it's cute you're gonna see it in a second and I really like the under boob on this and it matches my hair really good is that word called micro or something I don't know when you match all the colors I forgot what it's called I can't think of it this is the vibe I'm getting right now because my hair the shirt and it says nirvana I believe that's a rock band and it has like a cute little angel on it and like a skeleton body and this is the skirt it's cute if you or whatever see like I say they like the concept of it was like a full t-shirt to make like a top and bottom so yeah it's super cute it's it in the under boob we certain bitches can pull this off so if you don't have the confidence to pull it off don't get this set because it's definitely okay so this is the third outfit I already wore this because it looked it's so freaking good when I wore with my Bob it looked it so like I don't know motor sport ish I had to wear with it like I was so thirsty and y'all was going crazy on Instagram but I already knew where it was from cuz I already tagged them but this is the fit it's like a cute little motor sport body suit it's beads like it's so comfortable this material is just so stretchy I love it so much like in the material isn't tight it's like a I don't know like a loose soft stretchy fabric it feels so good it's so soft I love this so much like literally I would probably wear this and you can dress this up or down like you can throw on or club it don't matter it's still cute at the end of the day this is the next you know in every haul I gotta incorporate orange so this is like a cute little body set it's like a swimsuit type of thing going on because it literally is going up to here boo like past the pants like it's cute though so this is like a cute comfortable slide like literally you have your body suit your little jogging pants let me step back a little bit so y'all can see it's like a skinny leg jogging pan it's cute this is literally even me my emi vibes sneakers or sandals that don't matter and it does have the girls in the right so you love that cute orange skirt I really liked it the detailing on it that's why I got it it does have a matching top to it but I did not copy it I don't know why I just like it the bottoms better and I think I'm gonna just crop I mean it's a pair of this with like a cute crop top shirt or something like that like a bralette or like a crop top tank with like spaghetti straps I think will look so cute and yeah I didn't get this shirt and I shoulda but yeah so that's why I got so yeah this is a dress I'm scared to show y'all because my butt is not it's covered but it's pushing up so basically this is too fucking small so I don't know what the fuck I was doing with my sizing but but it just depends on the material but I really like this it says play what the fuck my letters is crooked as hell look at that is it supposed to be like that gay it's giving me mortar sport vibes it's giving me muscle shirt right now cuz it's fucking tight it's literally coming up girl like I can't even bend down cuz I don't have no underwear I'm gonna crop this into a crop top honestly because I could wear it to the club like cuz who cares what you wear to the club everybody being a whole club it's cute though I like it I love the concept but this material needs to be a little bit more stretch cuz it's not working out so this next outfit is my favorite cuz it gave me clueless vibes y'all are going crazy on me on Instagram so make sure you follow me on Instagram to see how I style all my clothes whatever get inspiration Regina George status I like today cuz it was different I didn't know where I was gonna wear it to but I'm like this is so cute like it's pink plaid it's like giving me super duper girly vibes cute adorable trendy fashionable I like it so I really really highly recommend it said this is probably my favorite out of everything so it says super trendy cuz this one shoulder make sure y'all shave your armpits cuz I did not when I took my Instagram photo but you can really tell that's why I had a sucker in my hand but yeah it's cute this is my favorite out of the whole bunch that I got so it's like overall type of feel with these adjustable straps you can adjust them so it can be like a bra type of fit or whatever and then it has these buttons right here and then it's like that material it's not velour or anything like that but it is soft and it's super comfortable this is definitely a cute little number and then it has matching shorts to go with it but yeah this is cute it's giving me barnyard bad okay barnyard baddie that's what I'm gonna put a caption as material so excuse that it has these detailing buttons just how you they just go like that and you buckle them this is like the Kim dress Kim K had on when she was in Miami like you I love this like this is my favorite I swear this gonna really give me a nigga real quick periods the material is super soft this dress is so flattering to my body shape it's okay y'all so this is the next fit it is a set but girl these pants do not fit make sure when you're ordering from this site go a size up cuz you can always get it altered but I don't know about getting these altered but this was a size medium and it's not been in and this would have been so cute it would have given me chilly girl group vibes so you know the vibe I'm talking about but yeah this would have been cute I'm kind of upset but that's all right but anyway that is one more item and I'll be right back this one is like a corset body suit girl I can not get this over my head I cannot get this over my thighs like it just not want to corroborate and it's so cute so mad because it has these metal bars in it and it's just not stretching out cuz it's like a corset but look at this material it's so shiny and cute like this is the cheap sheet that you can really make look expensive and I'm so fucking mad and they I'm so upset y'all bummer so actually the last item I had to try on was I wore this literally two days ago it's in a dirty clothes and it has so many makeup stains on it y'all I really have to get that out like I'm not showing y'all a looking tacky but I'm gonna insert a clip right here because the idea where it on Instagram already is so cute it's like this cute white dress with like a red color and then on the side of it it says babe right there let me show you I really don't want to try this on cuz on the body part it's just it's so much makeup it's ridiculous so it says babe right here and like this Barbie looking material that's why I liked it so much and then this is the color and it's super stretchy and then it's like a half zip to like you know get a little cleavage showing but yeah you can literally dress this up or down I dressed it up with some clear heels and a cute little bag it was cute so yeah all right y'all so that's it for this video I hope you guys enjoyed don't forget to like I subscribe and let me know in the comment section what was your favorite outfit of this whole haul I want to know cuz I want to know T what do you think says y'all opinions do matter to me so yeah don't forget to like comment subscribe I love y'all so much and I'm gonna see y'all in the next video bye


Hello. Myself Sunil Kalshatti, Assistant Professor, Department of Electronics Engineering, Walsh and Institute of Technology, Solapur. Today, I am going to explain the enhancement type MOSFET from the Industrial Electronics Learning Outcome. At the end of this session, students can describe the construction, working and characteristics of enhancement type MOSFET. Enhancement type MOSFETs are available in two types, n-channel e-MOSFET and p-channel e-MOSFET. Why the name is enhancement type MOSFET? Such type of MOSFETs are operates in only enhancement mode. They never operates in depletion mode. That's why the name is the enhancement type MOSFET. These are the schematic symbols for the n-channel e-MOSFETs, source, gate, drain. These are the three terminals, channel is shown in the above and arrow represents the direction of majority charge carriers from source to drain. Schematic symbols for p-channel e-MOSFET. As compared with n-channel, there is only one difference, direction of arrow, arrow is outward. What is the significance of gap in between the gate and channel? In this MOSFET, the gate is isolated from the channel. In between gate and channel, there is dielectric media, thin layer of oxide layer is there. That's why. The input impedance of MOSFET is very high and it is the significance of gap. Why channel is represented by the broken line in the e-MOSFET? In the depletion MOSFET, channel is present physically, but in e-MOSFET, the channel is present virtually. That's why in the e-channel MOSFET, the channel is represented by the broken line. Direction of n-channel enhancement type MOSFET. This is the construction diagram of e-MOSFET. It is the lightly doped p-type semiconductor material in which two heavily doped n-regions are diffused. That two heavily doped n-regions are represented by the source and gate. The body is represented by the substrate SS. In between gate and body, there is thin layer of oxide layer, SiO2 layer is there. So the input impedance of this MOSFET is very high. Here there is no direct electrical connection in between the gate and body. Working operation of n-channel enhancement type MOSFET. For this mode, VGS is equal to 0. If we set the VGS is equal to 0 and apply the positive voltage in between the drain and source. In the e-MOSFET, there is no connectivity in between the source and drain. So effect of this, there is no movement of charge carriers. So current remains 0. For this mode, the MOSFET operates in cutoff region. Operation with VGS is positive. Apply the positive voltage in between the gate and source and at the same time apply the positive voltage in between the drain and source. Now increase the VGS and VDS simultaneously. Here gate is at positive potential. So the electrons from the P region are attracted towards the gate but they cannot reach up to the gate. They are deposited near the oxide layer. As the VGS increases continuously, so continuously the electrons from the P region are attracted towards the gate and they deposited near the surface of oxide layer. At the specific value of VGS, the sufficient number of electrons are accumulated near the surface. So the channel is formed in between the source and drain. So connectivity is created in between the source and drain. So effect of this, the majority charge carriers electrons are start moving from the source to drain. So effect of this, the current flows from the drain to source. The voltage VGS, at which point the channel is formed, that voltage is called as a three-should voltage. To turn on the subtype of MOSFET, at least we should apply the VGS greater than VT. If VGS is less than VT, the channel is absent, so MOSFET remains off and once the VGS crosses the VT, the current starts flowing from the drain to source. So effect of this, the MOSFET operates in the ohmic region. In this region, the drain current is directly proportional to the VDS and it obeys the ohms law. There is a slight increase in VDS and there is a large change in current and the MOSFET operates in the constant resistance region. Effect of increasing drain to source voltage, when VGS is less than VT, the channel is absent and once the VGS crosses the VT, the channel is formed in between the source and drain. So effect of this, the majority charge carriers flowing from the source to drain and the current starts flowing from the drain to source and further if we increase the VDS, so current starts flowing from the drain to source. In this ohmic region, the current is directly proportional to VDS. So because of this current, the reverse voltage is developed across the channel, across the PN junction. So because of that reverse voltage, the depletion region around the PN junction is encouraged. So because of this increase in depletion region, the effective channel width is reduced. As the channel width is reduced, the rate of change of ID is reduced. At the specific value of VDS, the channel width becomes minimum through which high density charge carriers flows from the drain to source and the MOSFET enters in the pinch of region. The voltage VDS at which point the channel width becomes minimum and the current remains constant and further there will not any increase in current that voltage is called as a pinch of voltage or the saturation region. In this region, pinch of region or in the saturation region, the current remains constant. If you want to use the MOSFET for the voltage amplification, it must operate in the pinch of region or saturation region. It is the drain characteristics of young channel in MOSFET. The characteristics operates in the three different regions, cut-off region, ohmic region and saturation region. In the cut-off region, VDS is less than VT, current remains 0. In the ohmic region, the current is directly proportional to voltage. If you want to use the MOSFET as an close switch, then it must operate in the ohmic region and in the saturation region, the width of channel is minimum and the current remains constant and if you want to use the MOSFET for the amplification purpose, it must operate in the saturation region, which is the transfer characteristics of young channel in MOSFET, so transfer characteristics. It shows the relation between the VGS and ID. As long as VGS is less than threshold voltage, the current remains 0. Once the VGS crosses the threshold voltage, current encourages. As long as VGS is greater than threshold and less than pinch of MOSFET operates in the pinch of region. Once the VGS crosses the pinch of voltage, the MOSFET enters in the saturation region. In this region, the current remains constant, while enhancement type MOSFET is called as a normally off-device. In this MOSFET, when input voltage VGS is 0, then current remains 0. That is why E MOSFET is called as a normally off-device, why power MOSFETs are generally of enhanced SPED type. Up till now, we studied the depletion type MOSFET. In the depletion type MOSFET, when VGS is equal to 0, the current is maximum, but in the enhanced SPED type MOSFET, when VGS is equal to 0, the current remains 0 and MOSFET is in off state. And whenever the VGS crosses the VT, then and then MOSFET conducts. That is why practically, practically power MOSFETs are enhanced SPED type. These are references, thank you.


Welcome back, everyone. Thank you for sticking with us to this moment. We have two more sessions left. And next, we'll be having Olaide Joseph. And he'll be talking about transforming your Jupyter notebook to a reproducible machine learning pipeline using Kail. That sounds interesting. Let's bring Olaide up. Hi, Olaide. Can you hear me? No, I can't hear you. Let me see. Hello, can you hear me? Yes, I can hear you now. There seems to be some kind of echo. Not sure where it's coming from. Can you mute the one with the camera? Yeah, it's not appearing. It's muted. OK. I think it's better now. Thank you. So let me introduce you first. Now, Joseph is a machine learning engineer with a current focus on enterprise ML hubs. He currently works as an independent ML hub for cloud-mated ML hubs organizations. He has worked with ML hub organizations like Mavin Code and ARIKTO. Outside of machine learning, Joseph comes from an energy background, AirLangas. That's a big voice. His interest in energy lies in the adoption of technology to foster the energy-efficient operations. Finally, Joseph likes playing games and listening to dancehall music. That's interesting. We should talk more about the dancehall music after the session. Over to you, Joseph. OK, thank you very much. So I've been going over transforming your Jupyter notebook to a reproducible machine learning pipeline using Kailh, and I look forward to this session with you guys. So my name is Joseph. I have an idea like the host has said. I currently work as a contractor, ML hubs consultant with ARIKTO. So I work with the cloud-mated ML hubs company with products like Kailh, and he likes. So I'll be working on showing how the Kailh platform can be used to create reproducible machine learning pipelines. So to start with, let me just go over what this session is for, and if you are here, this session is for the GitHub scientists and DevOps with little on-line experience with Kailh flow. And what this session is about is we're going to cover, so it's depending on the time. So the plan is to cover this digital recognizer and notebook and Intel code chart and notebook. So depending on time, the focus for now is on digital recognizer. So we're going to look out for Kailh flow notebooks, specifically Jupyter Lab. Then we're going to look at the Kailh Jupyter extension with Kailh flow pipelines. So what this session is not going to cover, so this session is an in-depth machine learning or Kubernetes course, and this session is a Python programming course. So it's about the notebook will be converted into a reproducible machine learning pipeline. So it's the popular MIST or the digital recognizer notebook. It's on cargo for everyone, so everyone can have access to this. So about the notebook, so the question of this notebook was actually sponsored by my organization and it's an open source notebook. So you can actually have access to this notebook while I go over this session. So if you can see, this is the link, github.com, Kailh flow examples, Trima study under the digital recognition cargo competition. So I think I'll just wait for five seconds for people to just look out this link. Just the Kailh flow organization under the Kailh flow, you go to the examples repo, under examples repo, you go to the digital recognition cargo competition directory or folder. So pre-excited for this course, at least leads to knowledge about cloud computing environments like AWS, GCP, or Azure, basic understanding of cloud-dative architectures and community concepts like pods, controllers, nodes, container images, and volumes, etc. So familiarity with ML concepts, like algorithms, I expect those are attending the session to have a good knowledge of machine learning workflow. I mean, in machine learning workflow, you should be able to read your pandas, you should be able to read the CSV data, you should be able to like the feature transformation, you're able to do a parameter named model train and delight. So the bonus key flow as a service, bonus introduction to key flow fundamentals, that's like, by the way. So I'm not just going to key flow about you. So to start with, so I'm just going to give you some more of what Kubernetes is all about and why Kailh or key flow Kailh is like based on top of Kubernetes. So one of the key benefits of key flow Kailh leverages from Kubernetes is the potability, microservices and scaling. So when we talk about potability, we're talking about being able to like create a program somewhere and being able to reproduce the same program elsewhere, so not having to like have environment issues or dependency issues. So with microservices, what platform needs to like interact with multiple services and with Kubernetes, or cloud services. So we have scaling. So when it comes to scaling, we're talking about being able to like scale up the computing resources or use to scale down the computing which is depending on your workload. So this is like a brief overview of what Kubernetes tends to offer key flow Kailh. Let's talk about key flow itself and what key flow does. So key flow is an open source project that converges of cloud native and ML. So key flow is an easy ML ops platform for data science and operations team. It's a compute tool kit for an ML workflow, including data, model training, like parameter tuning, model setting and model monitoring. So key flow was launched by Google in 2017. It started at Google as a way to like externalize the TensorFlow Extended Experience, CFX, and it is Apache 2.0 licensed. So what kind of news does key flow aim to solve? So basically key flow aim to solve aims to like make your deployment of machine learning workflow on Kubernetes simple, portable and scalable. So the idea is that we need our Kubernetes as this orchestration power, this portability power and this auto scaling power already. So how can we leverage this power to like, so how can we leverage this power to make our deployment of machine learning workflow very easy? So which is where key flow comes into play. So key flow makes our data loading, verification, splitting, processing, future engineering, a parameter tuning, opportunity, observation and monitoring very, very easy. So we're going to go over the different tools and how key flow is like the all in one platform to do this and why it's like, it's best to use key flow. So basically, if you do your model building, you do it on Jupyter, you do it on Databricks, RStudio or Visual Studio Code, key flow offers you all this all in one. So if you do your model training, probably it's Python, SQL and TensorFlow, key flow also offers this to you. If you need to do a parameter tuning, you probably use SQL, Pistamine, AI or any scale, key flow also does this, auto ML, analysis, ML pipelines, metadata tracking and deployment. So key flow offers all these all in one. So it's just like your only one ML pipeline. So we talked about this key flow architecture. So basically in this session, this workshop, we're going to use the following components in key flow. We're going to use the Jupyter and the book. We're going to use key flow pipelines. We're going to use operators and we'll be looking at the central dashboard. So to go about notebook, the key flow has a notebook feature which gives you the same Jupyter notebook like field, the same Jupyter lab you have in your local computer, the same group of lab like field you have in your cloud internet IDE. So the key flow notebook offers you the same set of features. So we have Jupyter lab, RStudio and Visual Studio code embedded in key flow notebook server. So you can run any of these ideas to carry out your machine learning workflows using key flow notebooks. So we all have an idea of what Jupyter lab is because it allows more proprietary Jupyter notebook for Jupyter lab. So I won't really go into details what's Jupyter lab is all about. So it's basically just a web-based environment for creating Jupyter notebooks that's it. So Jupyter lab in key flow. So to go to the pipeline basics. So the co-focus of this session is talk about repressive machine, earthquake repressive machine learning pipeline. So before I go into earthquake repressive machine learning pipelines, what exactly is this pipeline itself? So a pipeline is basically a description of an ML workflow that includes all of the components in the workflow and how they combine in the form of an execution graph. So basically a pipeline is basically like your end-to-end machine learning process as a task or a step. For example, if you would like, if I, okay, just like, so if you would like to like create your ML, if you'd like to go through a typical model development cycle, a typical machine learning development cycle, you're probably gonna start from like your data ingestion or you call it data loading. Another data loading problem to do like feature transformation or you could want to like clean the data. After clean data, you do feature transformation. After feature transformation, you want to do model training. After model training, probably want to do a parameter training afterwards or you could do a parameter training while doing a model training depending on whichever works for you. And you want to do like model seven last the model monitoring. So in Kipto pipeline, you get to define all these things as steps. So at the end of the day, when you define all these different steps, it comes out in form of like an execution graph that you're connected to each other. Before, so for your data loading, you could, before you get to your, you start from a data loading, from a data loading, you get to your cleaning data step, from a clean data step, you get your feature transformation step. From a feature transformation step, you get to your model training step. After your model training step, you get to your model seven steps depending on whichever work for you would like to do. So Kipto pipeline offers you this feature. So in Kipto pipeline, each steps are defined as component, pipeline components. And what is the pipeline component? Basically, a pipeline component is basically just a set of codes designed to perform it as cloud task. So a pipeline component, an example of a pipeline component could be a data loading component. So what the data loading component does basically is just perform data loading operations. And as a part of the pipeline component is the model training component. What the model training component does is carries our model training, that's all. So pipeline components are self-contained sets of user codes packaged over the Docker image and the perform one step in the pipeline. So components may run severely or in parallel based on the way these pipelines are defined and we'll get to see all this in the Anzon example. So Kipto pipeline design goes with orchestration, experimentation, and reusability. So we'll get to like experience all this in the pipeline. Let me see what's time. Okay, sorry, I'm going to stop. Okay. So what's included in the Kipto pipeline UI? So you have like, what's included in Kiflix? You have like a UI. So the UI is similar to what is up at my right hand here. You have like your own notebook, tensile boards, models, snapchat, volumes, experiments, pipeline runs, recurring runs, articles and execution. So with the help of SDKs, we are able to, with the help of Kipto SDKs, we are able to actually create pipelines that can run and appear in the UIs. I did talk about the component, the component. This is an example of a Kipto pipeline. Like this is like an example. So you can see this is for the Chicago Taxi Trip data sets. It's a popular data set used in most Google tutorials. So basically, this is like data ingestion process and there's a pandas transformed data frame into a CSV format. There's a removed data format. So these are all these things that are checked out, all steps or components by the performing particular tasks or the performing particular operation. So basically what HGBoost train does like it trains an HGBoost model, what HGBoost predicts, it predicts on an HGBoost model, what calculate the regression matrix from CRG does is basically let's calculate regression matrix. So we get to actually see all these things are live in the unknown technical expression. Let me check this, see how far we can go. Okay, this is just 14 minutes. Okay, let me talk about experiments. I hope I'm communicating, can you hear me? So basically this is experiments, Kipto experiments. So basically an experiment is it works with where you can try different conversion of pipelines. For some of us from here with MLops, we all, and for those that are not familiar with MLops, I'll try to like just explain this thing.  Let's say I train a model with A is equal to five, B is equal to six and C is equal to 10. So the moment I change, the moment I change it to like A is equal to seven, A is equal to nine and C is equal to like 20. And I run it like I've generated a new experiment. So basically an experiment is where you, you are able to like implement new set of ideas and you still get to keep the pipeline work with the same and you get like different results. An example here is like, you can see this image. So this is an open vaccine project. So different experiments were created for the same set of work flow. So we have like pipeline run. So basically a run is a single execution of a pipeline.   So all these things that you might seem like a little bit overwhelming at the moment, you get to see it answer with this simple digital organizer notebook. So recurring runs, in recurring runs, basically you're trying to like repeat a particular run.  Let's say, okay, I want the run to like perform weekly. So with the record run feature, I get to like implement this. And this is actually very important in continuous training projects or continuous training work flows. Let's say you expect your data sets, you expect new set of data sets to come in weekly. So I mean, with the help of the recurring run, I don't need to like recreate new notebooks or recreate new pipelines. Recreation run helps me with this. So this will just go over this. So this is basically a step or you can call it a pipeline component. So if it performs, this is the load data step, pre-processed data step, model training step, model evaluation step. So I'll be going over the end zone session now. So let me go over, I'm going over the end zone session now. So first of all, before you can actually create this type of pipeline, you need to have Q-Flu installed. And this, there are different ways to install Q-Flu. You can install Q-Flu via a package distribution, you can install Q-Flu via manifest, or you can install Q-Flu via Q-Flu as a service. This is what I use, like I'm training my Q-Flu pipelines or my Q-Pipelines. So I would, so for those of us that are new to Q-Flu, they have no idea what Q-Flu as a service is all about. Probably need to like just go to Q-Flu. Theariplu.com. I think I probably registered. So you probably need to go to Q-Flu.ariplu.com. You see Q-Flu.com. Q-Flu as a service, okay, this is it. You can learn Q-Flu free Q-Flu cluster using Q-Flu as a service.  So for me, I've done that already. And I mean, it's the website as a very good UI and UX, I kind of explained things as well. So you should be able to like create this free service to use to back through. So there are different, like I said, Q-Flu, package Q-Flu distribution. So is that you install Q-Flu on AWS? You install your physical store on AWS? You install it on Google Cloud, Azure or other services? So the idea is that since Q-Flu works on the leverages Kubernetes for its operation, so before you can install Q-Flu, you need Kubernetes to be installed. So we have AKS, Azure Kubernetes service. We have GKE, Google Kubernetes Engine, and we have EKS, that's for AWS. So if it goes to the Q-Flu documentary or if you go to the Q-Flu documentation, I think it's quite easy. I don't know Q-Flu documentation website by app, so I could just go with this Q-Flu documentation, and I find myself, yeah. On that documentation, I'll probably click on installing Q-Flu, and it should be able to get me started. I mean, I think this was direct to me. Installing Q-Flu, so yeah. These are like different ways you can install Q-Flu. So you have different doves that can guide you through installing Q-Flu. Let's say you don't want to use Q-Flu as a service platform, you can't go through these routes. So this is Q-Flu as a service which I'll be using now. Think, let me just go back and get this sign in myself. Start with Q-Flu, yeah, learn Q-Flu free. If you have your system with QG, you can probably watch this process as well. So if you don't have an account, you can sign up. So let me have an account next. Then, yeah, this should work, we'll see if it could. So I'm coming out, I'll have to check this. Check my phone phone. So in the game, I'm going to stick on. I might guess it's, so let me just get off the chair. Verify, we'll see if we voice call next time. Okay, I think I got the voice call. Okay, so I just got a call from the code. So if you can, can you guys still hear me? Sorry, can you guys hear me? Okay, so. Okay, thank you. So once you've created the Q-Flu as a service, so it brings you here, all you need to do is create a new Q-Flu deployment. So I do have something running. I mean, the website is quite explanatory and it should be able to create this. So currently I have seven days left. So Q-Flu as a Q-Flu as a service gives me like two weeks free of this.  So my username is user and my password is my password.  So once you do that, it brings it to like a Q-Flu, it brings it to a Q-Flu UI. And I think I would like to open this and call it into Pitch, it doesn't have power with my... So I can still see the UI. Okay, so this is the notebook. So I do have a notebook server created. So what I'll do is I will go to the process and create a new notebook server. So I'm going to delete the one I had before. I'm going to delete this. I'm going to delete some experiments I had before running to create space. Okay, I think it's delete nine. Let me delete some snapshots I had. Um, yes, there's gonna be some snapshots I had. So, yeah, delete some of this book. So I'm currently in a new notebook. So my name, let me just call it KCD, KCD. So I'm using Jupyter Lab. So I'm probably going to leave it at Jupyter Lab. If I also use Jupyter, if I also use Jupyter, if I also use Visual Studio Code, I'll probably pick Visual Studio Code or RStudio, whichever one works for me. Okay, yeah. Set my CPU requirements. Let's say I want two. And I want two big ROMs, so I'm just CPU calls. So my ROM, I could set this to 10. 10 big ROMs. And I think that's all. So you launch. So this shouldn't take up to a minute. So while we wait for this to load, so I think if you are with your laptop, you probably just go through this with me. What you like. I'm seeing this record, I'll probably get it later. So, I mean, I'm going to go to this website, this GitHub repo, and I'm going to put on it. Let me just show you what I'm going through. So this is like an open source repo. I contributed to this and yeah. So what I want to work on is the KLGPtallab, the KLGPtallab, I'm just going to put on this repo. Copy link address. This is it, okay, just launch. WindConnect, something connected, we're going to open a terminal. Let's open a terminal. So this is like the same GPtallab environment you get when you look at a computer. So key to clone, for example, so I'll go to the digital recognition notebook, which is this. Now open the Qo notebook, which is this. I have a, so this is the digital recognizer Qo pipeline notebook. So I'll just, I will walk us through the notebook. It's basically just a markdown that talks about what cloud competition is all about. And yeah, so to be able to create these pipelines and that's like, so basically this notebook server I have here, the notebook server I created via the cluster, it's just like an empty environment. It doesn't have any, it just has a, just Python environment installed. So it doesn't have some libraries installed. So I need to go through the process of installing some libraries I would want to use. And the libraries I would want to use for this notebook, I would make TensorFlow, C++, Wget, and pandas. Since if that's, since that's the case, let me work through it. So I'm going to install, I've studied the requirements, the TXE file. So I'm going to install it. I'm going to pass the Criot flag, because I see all those standard outputs. So after installing the, it should take probably two minutes to like install. After installing, I'm going to like import all the necessary libraries. So to now walk with Q, to walk with Q feel Q, this is like where the magic comes into play. And I would like, I would like it if, if you've not been paying attention before, I think just probably just try as much as I can say, concentrate here. So while I'm waiting for this to install, I'll click on this Q extension here. So if you have a, if you are using the Qflow as a service, this thing, because the, you'd have the Qflow pipeline deployment panel for the Q deployment panel, yeah.   In my case, it is digit recognizer Q. The pipeline name is digit, digit recognizer Q as well. So the description, it performs processing, training and there, right. Training and prediction of digits. So the moment I enable the Q deployment panel, you see like the notebook changes. You are seeing some tags like skip, imports, pipeline parameters and delights. I'm going to just explain what these things are. So the beauty of QL is that it actually gives you the opportunity to like create components straight up from your notebook. Like you don't need to write extra codes or you don't need to do any extra. You just need to enable the Q deployment panel, create an experiment, come here. You see this like this pencil sign, you click on it. It tells you what kind of cell should this be. So basically, since I've already installed the libraries, I don't need to pass this into my Q flow pipeline again because I don't want to always install these libraries every time I run the pipeline. Since it has been installed already in my notebook server, QL is going to pick it up and you don't have to stress yourself about it. It's always installing every time you want to create a pipeline. So since I've installed, I'm going to have to skip the cell. So to skip the cell, you click on this and click on skip cell. From the word skip cell, just skip the notebook cell. Don't perform this operation, just skip it. Then I'm going to have to import my libraries. So I'll just make it a bit, and you see all your imports are in one place. So it makes scale be able to understand some of the libraries better. So when you call them or you want to use them down the line in your machine learning development process.  From the word imports, just everything you're trying to import into the notebook, just pass it here. Then for my pipeline parameters, so pipeline parameters are basically parameters that the pipeline uses or several components can use. For example, now I could use the batch size in multiple cases, so far as I've passed it as a pipeline parameter. So several components can use my pipeline parameter. It's like a global variable now in Python. So basically, I pass my pipeline parameter, this is like the parameter I want my pipeline to always have access to. Every component should always have access to this. If I choose to use this, if I don't choose to, I just want it to be there. So my pipeline parameter is generally my hyperparameter. So I have learning rates for Shares for LR, epochs, batch size, conversation dimension one, conversation dimension two. So I pass that. So I also set random seed, but in this case, I set it to skip it. So I don't want this to go into my pipeline graph. Imagine me having a graph that says, okay, set random seed, like it's not really, doesn't really look nice. I mean, you could do it if you want to, but I didn't just, I didn't do it. It's a choice. So I skip the cell. So that's now good to like creating a pipeline step itself. So everything we've been talking so far have been things like, have been add-on towards Q. And let's talk about the codes itself that perform these tasks, which is the pipeline component. So to create a pipeline component in Q, it's very, very easy and it's as easy as this. You have like specific commands that perform download data. So what I want to do then, I want to download this data from my GitHub, from the Kiplo GitHub repo. So I pass in the data link. I import the big get and zip file. I come to this pencil button, click on it. I pass in the step name, which is download data. So when you click on, okay, I click on pipeline step. So it tells me to pass in the step name and it's dependencies. So basically, what it means by dependencies here is which other steps is this step depending on. Since this is the first step in my pipeline workflow, it doesn't depend on anything. And so I just leave that for this one. So this is like my source code that downloads this on the train data, test it on the sample submission data. So in case if you're wondering how the source code is available, you can just check this is the source code here from this GitHub repo. You can share on the dates and you can create new ones yourself. This is source code. So everything that you're seeing me doing is in this GitHub repo for your use. So yeah, this is the download data step. So next up is the load data step. So okay, after downloading data, I want to load data into CSV, one that's unloaded data into like, load the CSV data into pandas as a data, loads it and outputs it as a data frame. So to do that, I'll click on this pencil sign. I'll call me and click on pipeline step. Passing the step name, load data. Dependency, it depends on download data. So what this just means is that like, I mean, it's going to load what the download data downloaded. So you see why I'm this kind of interconnected anyway. So I mean, the download data that doesn't benefit me downloads data. So that's like an ingestion process.  Or let's say there's a feature store somewhere that you load this data from, more like a data ingestion process. So this load data depends on what the download data downloads. So I mean, I read the data from, I read the data, the CSV data from what the data download data step are downloaded. And this is it. So the beauty of KEL is that multiple node cells can have a single multiple node that says can have a single pipeline step, can be under a single pipeline step. For example, the case here is the load data step.  This is also the load data step. If you can read this, it says leave the step name empty to make the cell to step to data. So basically you can make multiple cells to a single pipeline step. So the next pipeline step I have is pre-processed data. So it's as easy as, I was just coming to this page so I can click on this, go to pipeline step, click on pipeline step, come to the step name, enter pre-processed data, it's dependencies. So to pre-process data, I need what has been loaded. So it depends on the load data. It doesn't depend on the download data, it depends on the load data. So the load data information gets passed to the process data at the pre-processed data step, mixes of the loaded data, like carrier transformations and all. So there's a lot of pre-processed data, it's time for me to do my modeling.  It depends on the pre-processed data. So the pre-processed data gets passed into the modeling step. So yeah, what I just did, I create like my model, it's a TensorFlow, it's a convolutional model. So this is my modeling step, I have several new sales passed under my modeling step. This is my compilation because of time, I set it to like, what's the number of epochs I set it to? Okay, I set it to just two epochs. Take notes. So basically what I just did for my, for the parameters I passed into the pipeline parameters, before I pass them into my noodle cells, I tell the notebook what type of data type it is. In this case, while I was compiling, my learning rate is of data type flutes. And for some of us, if we are of Python background, we know what float is, or we know what float is in general. So this is like the decimals. So my learning rate is of type flutes, my batchize is of type integer, my epoch is of type integer. So I just pass it there and the notebook understands this. So the next step after modeling is the prediction step. So I just try to make this as simple as possible. And when it's time for the Q and A, I can ask questions. So the next step is the prediction step. So the prediction step depends on the modeling. So basically what has been passed to the prediction step is the model that has been fitted already. So the fitted model. And I make it as a fitted model to carry out predictions. And after my predictions, I get confusion matrix out of this. I believe we all have an idea of what confusion matrix is all about. After that, yeah. So I just taught my confusion matrix. So I'll be able to visualize it in the UI. So that's all about the, that's all about the question of pipeline. Let's see this coming to actualization. So once you've done all this, this is also the submission code. I have skipped it because I'm not submitting anything to NUS. So you can just skip this cell. So compile, when you're done, you can compile and run. So what Kail does is validate the notebook and it checks if your codes are correct. Once you see it, check them if your codes are correct. And it takes snapshots. So basically the snapshots, what Kail offers you, like once you've created, once you've created the notebook, it saves all the information, all the working environments, the storage inside your notebook server. It saves it inside Kail. In the event that, okay, let's say you want to use it next month, you want to use it in two months time. This information is really backed up for you. So once you're done, you get this view, whether you can view what your pipeline, how your pipeline is running. So I would want this to actually appear like on me. To view the actual pipeline, I think this is it, it should be there. So to view the actual pipeline, so what it does is it creates a volume first. After creating the volume, you see the download data step. So download data step is running. So when the download data step is done, you're going to get the load data step. When the load data step is done, you're going to get the pre-processed data step. When the pre-processed data step is done, you're going to get the modeling data step. When the modeling data step is done, you're going to get the prediction data step, which is the final step and you have your pipeline graph. So once I see this building issues like the issues the pipeline is now running, the issues the download data step is now running, and this should take about 10 minutes, 2000. So I think I can afford questions now. Why we wait for this front to finish? So basically the idea of this pipeline is that like, I mean, you want this process, since it's built under Kubernetes, in the event that like you are training something with a very huge workload, like it has the auto scaling feature because it leverages on Kubernetes, like to scale up based on computing resources and based on competing needs. Another feature of this pipeline is that, let's say you want to perform a continuous training operation for some of us that are from MLOs background, for some of us that are trying to get into like an MLOs background, you can create a pipeline that goes into like, you can create a pipeline that the output is a model that goes into a model registry. So depending on how you, I mean, this is like enterprise MLOs. So the download data step is completed. We are, this is the load data step, we have to wait for the load data step as well. So basically like I was saying, probably the output of your pipeline, it probably is a model that goes into a model registry. And then, thanks for before, you look at so far like a parameter training features, however, the focus of this session is not on a parameter, you know, model saving, or the life just on pipeline creation, possibly like some other time, about model saving with Kail or Kail through, talk about a parameter training using Kail or Kail through. So if you have a question, like we can start asking now, before we're done, the pipeline that have been put up, completed, I think that's like, that's all. If you have a question, you can start asking now. Hello, sorry, can I get in? Yes, we can hear you. I think we have a question. Is there a Kail extension for Colab? Okay, so Colab is not built on Kubernetes, it's not built on top of Kubernetes, so that would be possible. So there's no Kail extension on Colab. Okay. I think there's another question on how you can join the community. Although I'm not sure if it's KCB community or the Kail community. Okay, so the Kail community is still the CNCF community, Colab and CNCF community. So I feel like typically there's a key flow slot, probably just Google, I think let me see if I could Google this, key flow slot channel, key flow slot channel. I mean already, so I guess if it doesn't direct me to what I'm in. Okay, so this is key flow slot channel, you could Google these and the first thing that comes up is community bar, key flow, click on it and it should direct you to how you should be able to join the official key flow slot channel. I hope this is helpful. So this should be able to get all of this information there. Let's go to the pipeline. So the load data step is completed. We're going to pre-process the data step. So I have a demo question as well.  So your pipeline should look like something like this, something like this. You have like the create volume, download data, load data, pre-process data, modeling, prediction. So let's let me get out to this, this is it. So while this is, okay, pre-process data is completed. So modeling, second to the last panel, so let me start, let me copy this. So you could see the results live. So if you can hear me, I'm trying to like log in to like my private browser. So the display could like come out in full. So if I'm going to stop sharing now, I'm going to like request for access there. So I'm going to say everyone could see like the full pipeline. Can you see my, can you see my screen? It's coming up, okay. Okay, yeah. So like this is the full pipeline. So the beauty of KL again is that you could actually see some of the things you're doing. There are no visualizations. Okay, you could see some of the things you're doing like the model training strip. Can you see? I can't even look at it, so I should have something. Okay, yeah. For example, some of the, sorry, I think it's here for me. Give me a minute, sir. Lighty, if you are speaking, I'm not sure we can hear you. The output of your notebook, yeah, just as it is in your, just as it is in your notebook. Like when I call the dot ed operation, yeah, dot ed method, it's still the same thing I'm going to see, yeah. And the modern operation, I think I should see the model training step. Probably when it's done, you would get to see it. So it's just your entire notebook. Like, I mean, you don't get to like stress over all. I need to like do some things to like turn this into a pipeline. Like it's your entire notebook. The same way you have it in your co-labs, same way you have it in your GPTLA, but you just upload it here and turn it to a pipeline. So it's really typical for you to be able to see what you're doing. So typically, if you do this with the QVSDK, the traditional QVSDK, you would need to like write some components individually. And you would need to like run some methods, create clients, compile, or like this one, I just have my notebook. I don't stress about that. Plans to just click things, type things, click things, type things, yeah, I have a pipeline. And pipeline is actually very critical when you want like a reproducible ML workflow because every time I run this, I'm always going to get the steps. Like every time I run, I'm always going to get the steps. I could take this step on somebody's, on someone's like key flow course, different from mine. I'm still going to get the same thing. I could pass the output of my steps to like a model registry, still going to be the same thing. So like, this is like the beauty of machine learning on Kubernetes. We machine learning leveraging Kubernetes orchestration abilities, gain ability, capabilities, and the portability capabilities. So this is like my, the output of my, from my notebook, like the model, the summary, this is like the training step. And the last one is supposed to like show me my confusion matrix. So there are still other features which I didn't get to like show into this session. There are ways in which you could like output like pipeline parameters for some of us on to the experiment tracking. Like you want to like be able to compare different and pipeline metrics. I want to let's say, in each area of my first experiment, I go like 90% and I'm not running it. I'm getting like 89%. I want to be able to like track these changes. So these are some of the features you can also like get from like your pipelines on key flow in general. So let me see if I have something I've trained the past. Okay, then go to experiment. So these are, this is like the run UI. So these are some of my future experiments that have passed field experiments. Let me say experiment, go to experiment. So basically you get to see the metrics. Yeah, you get to be able to like compare these different metrics and it gets like we have to like rerun the metrics to the desired, the desired pipeline. Let's say you run about 10 different pipelines and you want to pick the best one based on your run. These are some of the things that you don't like taking into consideration, but that's not like the scope of these sessions. So we won't be going over there. But these are some of the things you could do with, you could do like experiment tracking, you could do like a pipeline orchestration with teach. You could do model seven with key flow. You could run distributed training with key flow. Yeah, I think that's it. So we have our pipeline run completed. So that's how to like create the pipe and machine learning pipeline using Q. That's all. So this is visualization. This is my confusion matrix. That's all. It's a good question because of the duration. Thank you, Eliade, for that awesome section. If you have any questions for Eliade so far, you can either drop it in the chat or you can also check him up on the session with their handles where you can ask for the questions just in case you're unable to get your response here.