How's it going my friends? I am so excited to be back here. Is this on? Am I good? Okay. They're giving me thumbs up I'm excited to be here. How are you doing my friend? I'm doing great. How are you? Why don't you introduce yourself? My name is Eric Boyd. I lead the AI platform team at Microsoft So the first question obviously and the most important that everyone has for you is are the robots taking over and how can we become their Friends the robots are totally nice. Okay. Okay. No, I mean that question is such a great one to get I've gotten variants of that. Can I hear some of these variants by the way? Please get your questions in because I'm sure I've gotten some Really weird questions about AI. You know, I was at an Onyx conference and Onyx is The open neural network exchange. It's something we work together with Facebook on And Amazon and NVIDIA and invited the whole ecosystem in as a way to exchange, you know models from one format to another But it is space. It's a file format. Yeah after sort of talking about Onyx I got a question that was well, have you ever had an Onyx model? You know like become self-aware and I'm just like it's a file format. That's really not how this stuff works. So it was Yeah, there's a lot of misunderstandings around what AI is how it works and you know, what people can do with it And so that's a lot of what we need to work through is AI can do some really incredible things particularly in the areas of Vision and speech and those types of areas But you know, it's not sentient. It's not self-learning like if you have an AI model that plays chess really well It's not gonna clean your floors. No, I've tried and it made a mess and my wife got mad. Yeah, I thought it made a mess I bet it didn't do anything. So here's here's the question for those that are out there They're like because there's a lot of AI this AI that all over the place for businesses out there that are like, okay I I think I need to take advantage of this. Where are some good opportunities to start? Yeah, that's a great question I spend a lot of time talking to companies about what are their opportunities and what are the things they can do You know, the the great thing about AI is it really is touching and transforming each and every part of every business And so even like the feature that we showed today in some of the keynotes the IntelliCode if you think about that that's such a small feature in Visual Studio right just reordering the way that the IntelliCode options come back But they were able to make that better and and you know by using AI and having a predictive model that knows What am I likely to be using it for they really made Visual Studio better? How much better a half percent better? but if you can do a half percent across all the different capabilities you can make so much better and so you know, I was talking to one company it was a You know a beer manufacturer and I said, well, how are you using AI and beer? Manufacturing and his answer was we don't use it at all in the beer manufacturing We use it in everything else in logistics in the supply chain and predicting the inventory that we're going to need and Forecasting the prices we're going to pay all the other capabilities that they had they were using AI models in those areas of their business and so really it's it's that's what's so interesting to me is just finding each business and how it's going to transform each of their businesses and really challenging companies you need to have the creativity to figure out How are you going to actually make your business better? Where can AI really work for you and really learning those different areas? So what is a good smell? I remember code smells. I I don't know where that turn came out But it's such a weird one know that term at all is a code smell is like you're looking at code And you're like this smells like something we could fix it or make it better Where are the AI smells so to speak in a business like hey, you know Maybe you should consider using this program because I consider AI a programming technique. That's right You know what are good cases for it if you have something where you know You are you're applying some rules like the sorting algorithm in IntelliCode right someone came up with a rule That said let's sort it alphabetically You can almost always find an AI algorithm that's going to predict or do that better Fraud detection is a classic place where people have rules if we're yet too many requests from this IP address is probably fraud AI is going to do fraud prediction way better if there are you know Financial forecasts I'm going to look at these three things about you know the the industry or the trends and sort of project What our finances are going to look like AI is going to do that better Insurance industry is getting completely redone if you think about how insurance works today they look at five different factors about your driving history or your car you have or maybe your neighborhood and AI can consider 10,000 factors Effectively I see and so those are things where AI is going to do a much better job of predicting And so yeah, those are all things that smell like AI would do well on it So tell me about the requirement the data requirement for AI like because a lot look I've had people come up to me be like hey, just do some AI and I'm just like Well, can you give me some data? Why is data such an important element of AI? Yeah What one of the first things I talked to companies about when I meet with them is if you don't have differentiated data You don't have differentiated AI So what is the thing that you have that special and unique about your business? That's going to make your business and your product better And so you know if a company is not collecting that today and a lot of companies I've talked to companies and they're like, oh we throw all that data away And you're like that's going to be the foundation that you're going to build your models and make things successful from But you've got to understand what is that differentiation? What is the thing that you've got that's unique about your business that you can build a great data asset on and Then build models on top of that so before you even start thinking about AI You should probably think about saving your data just making sure that and saving collecting and really looking for places where You have data where you can tell this was a good outcome or this is a bad outcome, right? If you take insurance this person defaulted on a loan this person didn't default on a loan So I can now use that to predict forecasting financials. I knew well I predicted very accurately based on this information. I predicted poorly here the more you have those those are the labels That's the stuff that's going to feed into your model And it's really gonna, you know be the way that your AI algorithm is going to learn And so if you have that then you've got the foundation to do something interesting awesome So you're using the term a lot model. Yeah, and for those that are maybe you don't know what a model is Well, how would you describe? What is a model? Yeah a model is it's really just a system of predicting What's gonna happen based on some inputs? And so I mean you can think of a really simple model You can think of Scott today Scott Guthrie was talking about I want to predict the price of a car And so a model could be as simple as You know, I take the year that the car was and I take the condition of the car and I multiply it by three And that's the price of the car. That'd be a particularly bad model An AI model is now going to take those same inputs and come up with a price Based on, you know, how AI sort of learned and so there are a lot of different ways You can represent models and think about them, but in a base. That's what a model is It's something that predicts something awesome. So we want all of your questions So make sure you get them in and I want to ask them for those that maybe don't have a lot of data And they want to do AI how can Microsoft help? So there are a lot of ways that companies can get started There are a lot of use cases depends on what people are trying to do if you look at their Speech, you know, if I want to add speech recognition to any application that I've got How could I do it? Well, I could go and collect a whole bunch of speech people talking and transcribe that and then build a model That predicts that or I could use a cognitive service, which is going to do it way better than what they're already doing So we have a wide suite of cognitive services that'll just really help accelerate people You don't need anything to go and use a cognitive service and you can do speech. You can do vision You can do language you can do search all of these, you know, right off the bat You can go and get started with them. Yeah, and it's really easy. I have it here on my screen You can go to azure.com front slash Cognitive I think it'll go right to it and like an easy one to look at is Is vision basically I'm clicking it right here And if you go to for example scene and activity recognition and images You basically just upload your picture Yeah, it's really simple if you have, you know, you want to be able to identify the objects in a particular scene Just upload it and it'll sort of show you the different things on it Then you can go further with that the custom vision service and you can say Everyone's favorite example if you watch the tv show silicon valley Not yet, but I will hot dog or non hot dog. Is that the one hot dog? No hot dog. This is a classic app It's really funny part of the the movie or the tv show You can build that in about I built it in two hours, but that's because I'm not a great programmer anymore I used to be what happened. I got old. Um, yeah I stopped programming as the main you know two hours though to build a hot dog non hot dog And it was really very good. I did did you use cognitive services? What I did is I used the custom vision model And so I went to bing and I searched for pictures of hot dogs And I found about 20 of them and I put them in there and I for for pictures of things There weren't hot dogs nice and there's your classifier and it really was very straightforward to go and build awesome So here's some questions coming in does microsoft ai supports self training classifiers So I think we do with auto machine learning. So automatic machine learning will do that automatic machine learning is Yeah, so I guess it depends a little bit what they mean by self training classifier Automatic machine learning is going to do a lot of that. What automatic machine learning does is You basically would specify here's the data set that I have And I don't know do I need a support vector machine? Do I need a neural network? Do I need a logistic regression? I'm not an ai expert. Please just figure out the right model for me And so automatic machine learning is going to do that for you. And so That's available today. You can get that as part of azure machine learning in the sdk It's one line of code and it'll go and build you a model just based off a data set I have the line of code actually right. I'm scrolling it. Here it is. It's it's literally. Oh, no, that's the deployment. Sorry That's just here it is. It's the auto machine learning. It's actually two lines But because one you define the problem and to run it. It's actually one line right actually really nice It's really straightforward really simple to use and we're finding a ton of customers who are using this And it really broadens the scope. There are really two ways. I see people using it One is, you know, if you're a data scientist, there's a lot of tedium that you go through in trying Let's try the support vector machine. Let's try a learning rate of point one and trying to change all these parameters Now you can just sort of say just give me a model. That's pretty good An automatic machine learning will just go and do that the other class of users are people who Don't even know what they would need to do and they can you know, if I'm a developer But I don't know how to do machine learning. I can use automatic machine learning and get a model Just from two lines of code. So let's see if I understand. So basically you have a bunch of data Yeah, I I usually usually I think of excel because it's the canonical I say excel and everyone's like, oh, I just pictured a row like a bunch of square of data And you say here are the columns I want to use to learn and then here's the column I want to learn you feed it in and then it does it and it's funny you say excel because in power bi In in we announced this is in preview that it'll be integrated as a wizard where you can walk through the experience doing exactly that And so create a model just from saying this is the column I want to predict and here's the rest of the data that I've got and that's pretty amazing I mean if you look at the code right here, hopefully you can go to the screen You can see that there is this data script That's basically says get data and in there you define And you can see my get data script right over. Let me go here real quick Get data script right here is basically saying here's the x and here's the y you want to learn you give it that And then it says I'm going to run a bunch of models. That's right. It goes through it tries all the different models It's really great. It's a it gets a little meta It is a machine learn model that predicts which machine learn model is going to work best on this set of data And the more you use it the more you use it on your sets of data It actually learns right from the history of how it's gone and predicted and it gets better Because it has a great answer of like well, how well did it predict against the test set? And so the more you use automatic machine learning The better it's going to work for your data the better it's going to work for you Which is really pretty neat and the cool thing about this and this and this is the meta bit Like it's using a recommendation algorithm. So basically like your shopping cart when you go shopping online and it recommends other products We have that ai when you run a model And it says oh it did okay. It starts to recommend others and it runs them. It's pretty cool It's exactly it's the same idea. It's not necessarily the exact same implementation It's the same idea is that yeah really cool really simple to use and I think honestly going forward. I think more and more stuff is going to start fitting into that automatic machine learning paradigm I think you're going to start seeing image classifiers work that way You know all the types of things that you're doing today More and more of it's going to fit into automatic machine learning and that's that's really our goal Is how do we simplify this stuff because there's so many people now who the demand to Have a machine learn model the number of people who want it the demand for a really high But there aren't enough people who can go and develop it and so how do we make it simple? So that any developer can go and build and and they'll have an ai model You have developers today if you think about I use the analogy of a hash table Developers use hash tables without even thinking about it right they probably learned about it in college And they studied what's a good hash function and things like that. No one's ever actually implemented that They just use a map and they just call it right. How do we get that? You know machine learning from the days where it is today where you kind of need to be an expert in linear algebra and calculus And you know back propagation to know this is a space. Here's my data push a button and go do it It's coming and we're going to get there But you know what it's still okay to learn linear algebra calculus because It is dear to my heart because I'm a nerd at heart I'm not wearing my glasses, but if I was you would see Love calculus. Well, and and this is what you're seeing a lot of people doing Everyone who's a developer out there today is learning more and more about how to be a data scientist and It much like the hash table understanding the fundamentals is really going to be important to doing it effectively Awesome. So it looks like tony from brazil is watching. We want to say hello to you my friend I have an idea in mind to build a face recognition for my business customer comes profile loads for receptionists Can azure help me with this? That's a great question So we have the cogniz service the face recognition api And so you can absolutely go and do that and have it recognize your customers and and suggest a profile of it I think it's you know, it's a really great idea, right a customer walks in and you say, oh, they typically order You know this particular cup of cup of coffee or something You can start getting it ready as soon as they walk in and so for your regulars. What a great experience, right? I'm coming back to that restaurant every time because they're going to be ready that much faster They see me coming and that's amazing like just with computer vision It will tell you the box around the face, right and then you can use in change chain that together with Uh with custom vision with faces of your customers And so you get the face box you take the box out and you give it to custom vision And now it'll tell you that's right. You can build up the profile and the history over time And so I think it's a really interesting use for how face recognition They're actually already our coffee shops in china that do this really so absolutely You you walk in and they they recognize you and they get your drink already So I think this is going to be coming more and more awesome So follow on question But do you have an auto classifier where the data does not have predefined data? So the auto classifier would group similar things I mean, that's a really good question. Like look, I'm an engineer and most people don't know this But I'm like an AI person. Yes, you are. So like right away when you say group things I'm thinking like k-means or hierarchical clustering or gaussian mixture models So there's already ways to do that. Yeah But we have the compute to do any type of machine learning model Tell us a little bit about auto Auto machine azure machine learning service and what that is sure So, you know, you talk about you need access to the compute to go and train your different models That's one of the, you know, why is AI really taken off has been three factors First is the amount of data that's available the advent of big data and product, you know Products like spark and and azure data bricks And now you can use those to really manage your large data sets The second you alluded to is compute and it comes in in two different ways One is it comes through GPUs, which have really accelerated the amount of parallel computation You can do and things particularly matrix multiplication as well as the cloud where these GPUs are really expensive And if I need 20 or 100 of them, I don't want to have to go buy them because I probably only need them for a few hours And so the cloud ability to go and use all that compute, you know For the time that I need and only pay for what I use has really transformed it And then the third thing is the new algorithms, you know coming up with, uh, you know Deep learning and convolutional nets and things like that has opened up a whole host of applications And so with azure machine learning what we're trying to do is bring that together and make it really easy for you to consume And so, you know, we do it through a python sdk So you can be in an azure notebook or a jupyter notebook or in vs code or in pie charm or whatever you like to use You're right there on your notebook and you can get access to start training locally change one line of code You know now i'm accessing a whole host of resources on the cloud And you know managing the data that's stored in basically any way you can store it on azure data everything from azure data lake to you know cloud db everything is all out there cosmos db So yeah, it's all really simple and integrated. So we started with uh, if you want to just get started quickly You should use cognitive services. Yeah, if you want to customize a little bit We have some custom versions of cognitive services. We mentioned custom vision. There's also others You can I think you can upload your own acoustic and language models for speech That's right You can use luis to start to tag your own data and get so there's ways easy ways to enter But let's talk now to those that maybe are more advanced that are starting to use like scikit-learn or pie torch or tensor flow Can you tell us what the current development process is like and how azure machine learning service can help? Sure You it's interesting the progression you walked with we think about it as cognitive services are You know microsoft's model microsoft's data the custom services are microsoft's model with the customer's data And then when I want to get to a custom model, well, then it's the customer's model and the customer's data And so, you know, how do you do that if i'm building a model? You know frequently what i'll do is i will Come up with the set of data that i have that i think has the best features in it And i'll come up with the algorithm that i think i'm going to use and i'm going to go and train it And then we call that an experiment and i go and run and i compare that experiment against the History that i've got the test data that i've got and i see how well i performed And what you find people doing is this iterative loop over and over Where they keep changing and maybe i need some more features or made this feature is going to make it predict You know in my car example Maybe the year is not very interesting but the location the zip code is or something like that What features can i add in and so azure machine learning will keep track of all the experiments that you've run So that you can see which one actually performed the best without and you know Basically keeps the history of what did you do different each time that you had it And then when i have a model and i'm ready to sort of deploy it Then the you know azure machine learning will put it in the model registry And so now i can keep track of all the models that i've got and as i deploy them I can understand where each model is and its life cycle is it deployed and which different systems And so i can manage that deployment and management of them much much simpler There are a couple of other capabilities that i think are pretty interesting I talked about sort of the the the life cycle of i'm going through all these different experiments A lot of times what i want to do is what's called hyperparameter tuning where each of these models There are probably a dozen different parameters the learning rate the number of nodes in each level All sorts of different things and so hyperparameter tuning is a way of helping you select the best ones Without having to sort of painstakingly iterate through them all yourself And so you put all that together and it dramatically simplifies the machine learning developer the data scientist In getting their models developed they become much more productive we talk to people who You know have spent weeks training a model and now they're like we can get this done in a day I can do this much much faster and the important thing to note is that Like look generally when i'm running these things i usually run it on my machine and then my machine is tied up And then I try to get someone else and i'm using pytorch 0.4 0.197 You know and sally's using pytorch 1 preview because she's all way more advanced than I am And it's hard to get all these things to to match up. How does azure machine learning help people that work in teams? And data science teams. What is it about azure machine learning that will help people work together? Yeah, I mean there are a couple things you can look at You know one is you mentioned like my machine's all tied up That's the beauty of the cloud right is I can now use extra computation on the cloud I can put it in a data science vm I can put it on the training modules a whole host of gpus and have sort of complete access to my machine The other is really the reproducibility I can take sort of each model and I can sort of run it the exact same way that you ran it And so yeah, you mentioned the different versions and all the different things that I'd need to do You know makes it much easier to sort of say Hey, we're all sort of working on the same thing and share the code And and really stay aligned with it and that's something that we've learned internally You know we have on our internal services thousands of developers working on the same model in bing And how do you do that effectively? How do you have a thousand people try and make improvements to a single model? You have to have all this infrastructure And so as we've learned from bing all that infrastructure that we built is coming through azure machine learning And that's what we're deploying in azure machine learning all the things we've learned in making that product much better Awesome. We'll keep your questions coming obviously any AI questions that you might have we want to get those in So there was announcements regarding AI today. Can you tell us about those? Sure, so a couple announcements that we made First and foremost azure machine learning is generally available I'm really excited about this. We've had a whole bunch of customers try it and preview And their feedback has been really really great. They think the direction that we're going with it has been fantastic We feel like the quality of the service the quality of our documentation understanding is all great And so we're happy to announce it as a generally available product And you know with that all the things that come with that automated automatic machine learning Which we've talked about is generally available the hyper parameter tuning the experimentation capabilities The model management the ability to deploy all of that service Generally available and something that people can go and take and use which is really exciting Another thing that we announced today is the onyx runtime So I talked a little bit about onyx at the start onyx is this file format for exchanging models and You know for really making it simpler for hardware manufacturers to to optimize them One of the challenges that hardware manufacturers talk us or talk to us about is they say look I've got tensorflow I've got pytorch. I've got chainer. I've got paddle paddle. I've got cafe I've got all these different frameworks and people expect me to optimize each and every one of them How do I do that simply and onyx says hey, you can just sort of make this an onyx model They can all convert into onyx and now you can optimize one of them What the onyx runtime is is this is the same runtime we've been using in windows in windows machine learning It's now available open source. It runs on linux. It will run on windows And it runs dramatically faster than sort of the native implementations And so we've seen internally, you know, virtually every model has run faster Some have the average is probably around two Two times faster But some have been as many as like seven or eight times faster. And so this is available It's open sourced and people can go and get it. I'm I'm really excited about that as well Another thing that we announced the third sort of major areas around cognitive services We are the only company that does containerized cognitive services People want to be able to run a i models and they don't want to just consume it in the cloud Sometimes they have latency requirements or they have intermittent connectivity for some machine that they're trying to run it in And so they want to be able to take the model and run it on prem or on the edge And so we announced containerized models and today we announced that the language understanding model is also available in a container Amazing louis louis is now available in containers. So that's amazing. I was recently like I was recently in in new zealand And one of the hospitals there they wanted to use cognitive services to do ocr But they couldn't do it because they could not the records could not be uploaded to the cloud This really enables them to start to do that work. That's exactly one of the use cases we see is You know regulations prevent them from moving the data And so being able to bring the the cognitive service directly to where they actually have the data Opens up all kinds of doors that previously they couldn't do before and so We have had a ton of positive feedback from customers on it. People are really excited about this So i'm excited to see that going out. It's basically the first ever lift and shift down that i've ever seen. Yeah, it's interesting all the you know Everyone is trying to lift and shift up to the cloud and and look that's a an important trend because the cloud offers a lot of advantages But as a company we've been very committed to the hybrid and making sure we work with people Where they need to be and often that means hey some of the things we've done in the cloud need to happen On the edge there are real legitimate requirements. Why that needs to be done And so we want to make sure that that that support like just Basically and here's a really silly use case that I thought of if you run a parking lot and you want to have an unattended parking lot And still charge people you can have a local model Taking picture as cars drive in with ocr getting license plates. That's right All sorts of examples like that where you know, we hear of manufacture of companies that have manufacturing plants that You know their connectivity comes and goes right They have vehicles that they want to drive out into the field that they might not have any connectivity at all And so to be able to still run models locally These a lot of great examples with drones They want to fly drones along power lines and sort of see hey are there defects in the power lines and things like that Things they need to go repair you get if someone drive for thousands of miles You could fly a drone and have a take picture and say this is where you need to go to And so yeah, it works great. So here's a couple questions Well ml.net expand to training framework So I don't have to learn python num pi tensorflow pi torch and I can rather stay in c sharp where I feel at home so ml.net has a whole host of algorithms where you can You can train models directly in c sharp in ml.net ml.net again grew out of internal technology that we had where a bunch of internal developers used c sharp and wanted to have a good framework for developing their models in c sharp And so we've wrapped it up and made it ml.net as something that now you can go and and train models in c sharp You know, we have a ton of developers in c sharp that are really not well served By the ml community because the ml community says everything has to be in python And so being able to have ml.net as a way that you can now stay in c sharp Is really pretty interesting for people and uh, you know There's still a ton of activity happening on python So I wouldn't necessarily discourage you from learning python because that's where there's a lot of value being created But absolutely ml.net is going to let you stay in c sharp. Awesome next question AI is more than just image recognition. What about searching for text and dynamic images? Current ocr engine is not accurate in cost per transaction forces us to use on-premise solution Interesting, so i'm not really sure what they mean about not accurate or dynamic images Yeah, I mean Maybe in videos or something. I mean so the the ocr solution that we have Um, I get benchmarks on all of our uh, I don't know services on a weekly basis We're beating everybody else with ocr. So I believe we have the best ocr solution that's out there It really performs quite well And uh, you know, we have uh, I talked to our researchers They are very proud of the techniques that they've used as some of some of the tricks that they've done in their models To make it really perform so well You know, additionally with images, you know, you sort of talk about finding Different things within the image. I mean, I love the example today that you had in the uh, the nba example Showing really the face detection working on all sorts of small areas There's also logo detection right and being able to find the logos in all sorts of different places in it Um, and one of the places where we really pull all this together is in knowledge mining So you take exactly what you were doing and now you've got you're extracting all this information from across an image and some of it Is ocr some of it is looking for text in an image Some of it is face recognition on the image Some of it is sentiment analysis on the image or on the text or whatever it is I'm building an index that's now searchable and brings all the you know The named entities that you can sort of think of whether they're people or places and what's the relationships between them You can bring all that together and so knowledge mining makes that really powerful Now and I will say uh for for you that that submitted this question Like if it's not working for you, can you email me and I'll look at it because I've seen our ocr engine get like Words behind chain link fences that say stop that are crooked and give you the right bounding box It's ridiculous. I've even seen it. There's uh, you love the blacked out text where they used a magic marker And you can still kind of see the text underneath of it and our ocr is telling you what the text is underneath of it Which is really very cool. It's amazing Okay So tell us a little bit about some of the exciting things that you see some of the customers doing because One of the problems with this technology is people have a hard time like saying they once they get it They're like, oh, okay. I can see how this works. Yeah, but they have a hard time seeing where it might fit In their in their business. What are some exciting things you're seeing customers? You know, there's been a lot of interesting examples You know, one of the things I maybe more fun ones is You know, we've talked to shell and so shell has gas stations, of course all around the world And so they put cameras in their gas stations and what they really want to detect is fire, which is a big problem at a gas station But what they also are detecting are is the person who's filling up with gas is he's smoking And if they are they want to alert the attendant say hey go yell at that person So it's really dangerous to smoke at a gas station. And so lots of applications like that We see a ton of image classification applications Companies, uh, you know jable is looking they they manufacture Chipboards and so the last step they wanted to sort of take an image of it and see Do we miss any of the solders in it? Is there any any defects we can see with this? And so just improving the quality that they have there We see a ton of bot solutions People are looking for bots, you know, often in customer support Can I deflect, you know 20 of my customer support costs and save myself 20 million dollars while giving my customers a better experience? We see virtual assistants where people want, you know, their branded experience their voice You know sort of interacting with a customer in a virtual assistant way. So we see a lot of solutions there Um, so yeah, just a ton of applications across the board. Awesome. So we have a question coming in How could I use AI to help with accounting such as invoice coding? So I'm not an accountant and I'm not entirely sure what invoice coding is, but I'll guess You know, presumably it sounds like a classification problem, right? I have an invoice come in and I need to decide Which budget do I charge it to or which type of invoice it should be or anything like that? There are a number of different ways you can do it depending on sort of how your invoice comes in One is you can do image classification if you have an invoice that sort of looks like image one or an image in something else It looks different You can do it that way the other is to build sort of tech classification and sort of understand What type of of system this is but that's a standard classification problem And uh, that's the exact example of what I'm talking about of each business and each industry needs to find the ways That things are changing for them and how they can really use it to make their business better Where you know, if you can speed up your your classification on your accounting invoices and have better accuracy with that You just get so much acceleration from that as a business And so really finding those ways that have really getting the industry to have the creativity to understand What are the places they should be using this technology? That's going to be one of the most exciting things over the next few years I found earlier my early in my career as a programmer Anytime someone wanted me to optimize something. I would look for tasks that someone repeated exactly the same way often That's right Now I feel like when I'm doing ai my particular smell is if someone is like altering code Like 0.7 to 0.5 or adding extra if statements and then deploying Yeah Like for me when you're doing those kinds of tweaks where you feel like you need to take a shower afterwards as a coder That's a good place to start thinking about using ai they're using rules to make a prediction That's what you're sort of describing should it be 0.5 as a threshold should it be 0.3 as a threshold? And uh, you know, this is where I say you saw this in fraud a lot If I get a thousand requests from the same ip is probably fraudulent Um, and I you know, I remember the early days when I was working on fraud You know aol dial-up modems were a huge problem. It tells you how long I've been working on fraud Because they all came from the same ip address And so you'd shut down all of aol from that one ip address And ai and ai model is going to be much better to learn that hey They're different patterns that I should be looking for and so instead of having these broad course rules I'll have these you know, I can really pull in a thousand different features in and build a model around that Let's talk a little bit about Sort of the elephant in the room and this is important because people are looking at this and they're like Hey, how much is this going to cost if I want to use if I want to build my own machine learning model and azure machine learning service is this going to cost me a lot So the beauty of using the cloud is that you use only what you consume And so the cost for gpu's if you were to go out and buy a whole host of them They're tremendously expensive and so the rates that you can use when you're using the training service You know is measured in you know the tens of cents per hour and things like that so dramatically cheaper You know building models. Yeah, there's costs associated with it, but it's it's not exorbitant This is something and the benefits that you get from it on the other side I don't think I've seen anyone come back and say hey We're not going to build this model because the the training costs are too high And here's the thing like I've I've laid down an azure machine learning service workspace And we basically lay down four things we lay down app insights, which is nothing. Yep. We lay down storage Which is empty. Yeah, right. We lay down acr, which there's a free version.  Oh, yeah, the compute. Yeah, uh, the compute stuff And like you and even the compute isn't laid down until you specifically specifically ask for it And so you can also create a compute environment that has zero to end nodes. Yeah, and it won't even run Yeah, I mean, that's you know, I honestly, I don't know how everyone doesn't develop on the cloud these days The the economies of scale that you get from being in a cloud are just they just give you such a better Efficiency from that. It's pretty amazing. Okay. So we've got about three or four minutes left Where can people go to find out a little bit more about This ai stuff and how can they like get started like if you're a programmer and because you already know about all this Ai stuff, what would you suggest someone go do right now? I mean the things that the easiest way to really get started is to go and look at the azure notebooks And start with there the azure notebooks will walk you through how to use azure machine learning service Notebooks are such a great environment for learning in Because there's a description of what you're trying to do right in line with the cell that executes the code You can change the code and continue to execute it and make it just go right there in line Um, that's the thing that I would recommend everyone go and do it'll both, you know Depending on sort of what you need to learn if you need to learn how the services work. They're a great document They're great, um notebooks should have walking you through how to use the service if you need to learn how to build ai models There are notebooks that'll show you hey, here's some of the simple ai models Do I want to do like, you know the classic MNIST? I can type draw Number and sort of have it recognized Um, you can those are really straightforward ways to go and do that I just remembered it's key vault is the last one. So there's four things really down storage App insights key vault and azure azure acr, right, which is azure container Okay, so I figured I'd show just a little bit of what this what this looks like because a lot of you are probably wondering What this looks like and like it's really simple And the cool thing is that the part that maybe we haven't seen in francesca showed that a little bit during scott's Demo is we actually have these amazing integrations individuals geocode where I can go in and submit experiments by right clicking right a viewer experiments Or attaching right and and that's that the coolest part right and The other thing that I really liked about this that I've used so far Is there's a team of like four or five of us that work on these models and all of us can see what all of us are doing Yeah, like here's an example that you talked about hyper parameter tuning. Yeah, it ran 20 experiments And notice that here there's like this green line It actually stopped running it because it's like, yeah, this one didn't work This hyper parameter is not working and that's cool because initially like usually I'm running a for loop and Spanning over hyper parameters like the learning rate or momentum or whatever Here it's a smarter for loop because if it's within and I use the bandit method if it's been 20% It's going to kill it now And that's a great example of you know, usually you'd have to sort of be sitting there watching and tracking having it Basically printf. Hey, this is what's good. This is the learning rate. How it's converging You know hyper parameter tuning is just going to do that for you and so it's it's really saves a ton of time for you And the other thing is and this is the part that's really cool. Here's all the compute environment You can see on my computer. We have like some batch ai We have some kubernetes services in our compute and it's basically we just submit and forget And then we can see all the output. It shows us all the output and notebooks It's amazing Now here's the the part that we were talking about models to me are like the executable part of ai Yeah, that's right. It's like it's like when you compile your code you get this assembly I feel like that's what a model is too. It's a great analogy You put something in and something comes out and as a programmer It's something that we want a version. Yeah, and in here you can see we are versioning all of the models That's right And then we can marry models with scoring files to create images and then we can do deployments And so for example, you can see the simple emnist service right here I wrote like a cheesy little app here Where I submit like I can draw a number. This is the cheesiest version That's easiest thing, but you can see it returns things from me directly from the service And I'm going to make like weird numbers and you can see that. Oh, it really thinks it's a four But if I start to do crazy things like this You can see that it's going to start to be sure about other things, right? And it's it's pretty cool that you're able to go from idea To submit a job To save a model to create an image deployment all within the same environment Yeah, I think it's really important the the software development life cycle For models is different than for software. Oh, it is and so understanding What are the tools that I need and how do I really use that more effectively? Those are the things that are going to make you productive and successful In building your models and deploying them if you if you try and do it, you know Sort of the standard software way you're going to have a hard time Figuring out how it all fits together. Awesome. Well anything else to finish up with my friend I'm really excited that azure machine learning is GA. I think there's going to be a ton of amazing uses for it And I'm excited to see what people will do with it. Well, thank you so much for being with us, Eric All right. Well, the show is not over. We have my amazing colleague brian bands just over there with beth To talk all things java Let's go to that