Global AI Back Together - ML.NET, Serverless Functions & Integration
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This document summarizes Luis Beltran's presentation on machine learning with ML.NET and serverless functions. The presentation covered ML.NET for building machine learning models, Azure Functions for serverless applications, and a demo integrating the two by calling an ML.NET model from a function.
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Global AI Back Together - ML.NET, Serverless Functions & Integration
- 1. Global AI Back Together Luis Beltrán Microsoft MVP Hosting Partner Serverless Machine Learning
- 2. Luis Beltrán Microsoft MVP in AI and Developer Technologies Researcher at Tomas Bata University in Zlín, Czech Republic Lecturer at Tecnológico Nacional de México en Celaya @darkicebea m luis@luisbeltran.mx luisbeltran.mx
- 3. Agenda Introduction Machine Learning (ML.NET) Serverless (Azure Functions) Integration (Demo)
- 4. Introduction
- 5. .NET Your platform for building anything Desktop Web Cloud Mobile Gaming IoT AI/ML
- 6. COMPILERS LANGUAGES RUNTIME COMPONENTS LIBRARIES INFRASTRUCTURE .NET TOOLS VISUAL STUDIO CODE CLI VISUAL STUDIO VISUAL STUDIO FOR MAC WEB CLOUD MOBILE GAMING IoT AI/ML DESKTOP
- 8. Historic data New data Machine Learning Algorithm ML model Data Prediction Training Prediction Machine Learning Machine learning is a branch of AI that focuses on building apps that learn from data and improve their accuracy over the time without being explicitly programmed to do so.
- 9. Learning Tasks Regression Class A Class B Classification Clustering
- 10. ML.NET is a framework for custom ML Machine Learning lifecycle Historic Data Test / Evaluate Build &Train ML model .ZIP file (Trained) Prepare Data, Build and Train an ML model ML.NET API (.NET Console app, etc.) ML.NET Model Builder (UI Desktop Tool) ML Tasks, Data Transforms, Learners/Algorithms ML.NET API (.NET app) API to run the model nuget nuget Business Application (Web/Service/Desktop/Mobile) ML model file .NET app Predict: Run/consume the ML model ? Live User’s Data ML.NET API to consume the model Run / Predict .NET .NET .csv files Database etc.
- 11. What can you do with ML.NET?
- 13. Most scenarios Microsoft.ML Forecasting & anomaly detection scenarios Microsoft.ML.TimeSeries Recommendation scenarios Microsoft.ML.Recommender Database loader System.Data.SqlClient Consuming ONNX models Microsoft.ML.ONNXTransformer Microsoft.ML.ImageAnalytics for object detection Consuming TensorFlow models Microsoft.ML.TensorFlow SciSharp.TensorFlow.Redist Microsoft.ML.ImageAnalytics for image classification Train custom image classification models Microsoft.ML.Vision Microsoft.ML.ImageAnalytics SciSharp.TensorFlow.Redist .NET Standard .NET Core .NET Framework
- 14. MLContext MLContext is the starting point for all ML.NET operations and provides ways to create components for: ◦ Data preparation ◦ Feature engineering ◦ Training ◦ Prediction ◦ Model evaluation ◦ Logging ◦ Execution control ◦ Seeding
- 15. Data Schema IDataView It’s the set of columns, their names, types, & other annotations Before loading data, you must define how the schema of data will look (column names & column types) Use class definitions to define IDV schemas Dataset Schema class definition IDataView Label SepalLength SepalWidth PetalLength PetalWidth Iris-setosa 5.1 3.5 1.4 0.2 Iris- versicolor 7.0 3.2 4.7 1.4 Iris-setosa 4.9 3.0 1.5 0.1 …
- 17. ML.NET Model A serialized zip file which contains data schemas, data transforms, and algorithms ML.NET Model MLModel.zip Desktop Web Mobile
- 18. Demo MACHINE LEARNING WITH ML.NET
- 22. Serverless application platform components
- 23. Azure Functions
- 24. Your code runs when a (previously-specified) event occurs Blob Storage Cosmos DB Event Hub HTTP Queues Service Bus Timer Webhook Photo taken and sent r Upload it to blob storage PP r Produce scalated images File Table Excel OneDrive Email Mobile app Notification Más…
- 25. Use Bindings in Your Code public static void Run(byte[] image, string filename, Stream outputBlob, TraceWriter log) { log.Info($"Processing image: {filename}"); var imageBuilder = ImageResizer.ImageBuilder.Current; imageBuilder.Build( image, outputBlob, new ResizeSettings(640, 400, FitMode.Max, null), false); } { "bindings": [ { "name": "image", "type": "blobTrigger", "direction": "in", "path": "card-input/{filename}.jpg", "connection": "AzureWebJobsStorage" }, { "type": "blob", "name": "outputBlob", "path": "card-output/{filename}.jpg", "connection": "AzureWebJobsStorage", "direction": "out" } ] }
- 26. Demo INTEGRATION OF AZURE FUNCTIONS + ML.NET
- 27. Q & A
- 28. Thanks for joining! LUIS BELTRÁN ABOUT.ME/LUIS-BELTRAN
- 29. Global AI Back Together Thank You Hosting Partner Register to Win Gift hamper
Editor's Notes
- ML: Uses mathematical algorithms that can teach itself to evolve while data changes. Supervised Labeled data. Non-supervised Pattern discovery using non-labeled datasets.
- An open source and cross-platform machine learning framework for .NET Use your C# or F# skills and knowledge to enable ML in your .NET apps How to improive a model More different data Larger datasets Specialized datasets Mixed datasets Different pipelines Different learners Different transforms Different hyperparameters
- ML.NET API (code) ML.NET Model Builder (VS) ML.NET CLI
- Data in ML.NET is represented in an IDataView
- IEstimator defines the operations that will be executed. ITransformer executes the operations
- On-premises What media should I use to keep backup? What is the right size of servers for my business needs? How do I deploy new code to my server? Whay happens in case of server hardware failure? Who monitors my app? What about server security? Which OS do I need? Fortunately virtualization came to the rescue. One great thing about the cloud evolution is that each phase leads to less waste. Infrastructure as a Service leverages hardware in a way that you can stop worrying about the hardware and focus on the operating system and your virtual machines. You can get better utilization of your resources and it’s faster and easier to provision new instances. IaaS addresses many of the questions we dealt with on-premises, and more importantly paves the way to the cloud. Platform-as-a-Service is the next evolution: host a runtime, so we stop worrying about hardware and operating system and even dependencies and just focus on our project. We’re no longer in the business of constantly patching an OS or even making sure dependencies are installed to run a web server or host a process. PaaS simplifies the process even more and provides a mature platform for code that must run at scale because you can still size (scale up) and specify instances (scale out) to manage your workloads. Serverless takes us to the ultimate cloud native experience: we simply focus on code. It doesn’t mean there is no server, because we still need to run on infrastructure, but the infrastructure is further abstracted so there is less server in our scope of responsibility and more code. We focus on what is unique for our business and spend more time on innovation, less time on answering the myriad questions that exist with the previous phases. Serverless is cloud native. But what does that really mean?
- Write your code in C#, nodeJS, Python, F#, PHP, PowerShell, Bash or Batch You can extend your functionality by using NuGet or Node PM You can even include DLLs to be called from your code or EXEs and execute them
- Other scenarios Devices feed data such as temperature, into Stream Analytics, then AF or Logic Apps cleans, processes and transforms this raw data into structured data that is stored into a database. Create a Timer Trigger function in order to schedule a function that runs at a specified frequency, for example every 15 minutes or exactly at 9 am in Europe every Monday to connect to a database and clean it, for instance to remove non-valid data