Introduction to ML.NET
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Slides for the AI community meetup organized by Deltatre Innovation Lab, in Turin, November 19th (OGR Tech, Talent Garden).
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Introduction to ML.NET
- 2. #DeltatreInnovationLab Clemente Giorio R&D Senior Software Engineer Gianni Rosa Gallina R&D Senior Software Engineer
- 3. Deltatre powers the world’s top fan engagement experiences Global team of 1,000+ experts delivering technology and user experiences across 19 offices Over 30 years of business with hundreds of awards EMEA Turin London Geneva Hamburg Munich Paris Prague Brno Skopje AMERICAS Los Angeles New York Utah APAC Mumbai Singapore Tokyo Sydney Hong Kong A B O U T D E L T A T R E 3
- 5. DESKTOP CLOUDWEB MOBILE ML .NET Your platform for building anything IoTGAMING
- 6. “It has exquisite buttons … with long sleeves …works for casual as well as business settings”{f(x) {f(x) Machine Learning “Programming the UnProgrammable”
- 7. f(x) Model Machine Learning creates a using this data Machine Learning “Programming the UnProgrammable”
- 8. ML.NET 1.0 Machine Learning framework for building custom ML Models Custom ML made easy Automated ML and Tools (Model Builder and CLI) Proven at scale Azure, Office, Windows Extensible TensorFlow, ONNX and Infer.NET Cross-platform and open-source Runs everywhere
- 9. A few things you can do with ML.NET ML.NET tutorials GitHub samples
- 10. Building blocks for a Data Science Project Data sources
- 11. 1. Data Example Comment Text Sentiment Wow... Loved this place. 1 Crust is not good. 0 Not tasty and the texture was just nasty. 0 The selection on the menu was great. 1
- 12. Text Featurizer Featurized Text [0.76, 0.65, 0.44, …] [0.98, 0.43, 0.54, …] [0.35, 0.73, 0.46, …] [0.39, 0, 0.75, …] Example Text Wow... Loved this place. Crust is not good. Not tasty and the texture was just nasty. The selection on the menu was great. 2. Transformers
- 13. Example Estimator Comment Sentiment Wow... Loved this place. 1 Crust is not good. 0 Not tasty and the texture was just nasty. 0 The selection on the menu was great. 0 3. Estimators
- 14. Comment Text Sentiment Wow... Loved this place. 1 Crust is not good. 0 Not tasty and the texture was just nasty. 0 The selection on the menu was great. 1 Yelp review dataset Features (input) Label (output) Sentiment Analysis Is this a positive comment? Yes or no
- 17. Building blocks for a Data Science Project Data sources
- 18. What is automated machine learning? Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data.
- 19. Automated ML Mission Democratize AI Scale AIAccelerate AI Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI Enable Domain Experts & Developers to get rapidly build AI solutions Improve Productivity for Data Scientists, Citizen Data Scientists, App Developers & Analysts Build AI solutions at scale in an automated fashion
- 20. Machine Learning for Everyone Automated Machine Learning Data Scientist Citizen Data Scientist Data Analyst Data Engineer Developer
- 21. How much is this car worth? Machine Learning Problem Example
- 22. Mileage Condition Car brand Year of make Regulations … Parameter 1 Parameter 2 Parameter 3 Parameter 4 … Gradient Boosted Nearest Neighbors SVM Bayesian Regression LGBM … Mileage Gradient Boosted Criterion Loss Min Samples Split Min Samples Leaf Others Model Which algorithm? Which parameters?Which features? Car brand Year of make
- 23. Criterion Loss Min Samples Split Min Samples Leaf Others N Neighbors Weights Metric P Others Which algorithm? Which parameters?Which features? Mileage Condition Car brand Year of make Regulations … Gradient Boosted Nearest Neighbors SVM Bayesian Regression LGBM … Nearest Neighbors Model Iterate Gradient BoostedMileage Car brand Year of make Car brand Year of make Condition
- 24. Which algorithm? Which parameters?Which features? Iterate
- 25. Enter data Define goals Apply constraints OutputInput Intelligently test multiple models in parallel Optimized model
- 27. Data Preprocessing Feature Engineering Algorithm Selection Hyper-parameter Tuning Model Recommendation Interpretability & Explaining 1. 2 3. 4. 5. 6. Automated ML
- 28. Class imbalance Train-Test split, CV, rolling CV Missing value imputation Detect high cardinality features Detect leaky features Detect overfitting Model Interpretability / Feature Importance
- 29. ML.NET AutoML
- 30. Automated Machine Learning (AutoML)
- 31. On the command line, with the ML.NET CLI mlnet auto-train --task binary-classification --dataset "yelp_labelled.txt" --label-column-index 1 --has-header false --max-exploration-time 10
- 32. With a graphical user interface, with the the ML.NET Model Builder
- 33. https://github.com/marktab/machinelearning- samples/tree/master/samples/csharp/getting- started/BinaryClassification_AutoML Via an application, with the automated ML API
- 35. What’s new?
- 36. ML.NET v1.4 (announced at Microsoft Ignite) Image classification based on deep neural network retraining with GPU support Support for Native DNN (Deep Neural Network) transfer learning with ML.NET Improvements for Image Classification • GPU support on Windows and Linux • Predictions on in-memory images • Training early stopping • Learning rate scheduling (Exponential Decay, Polynomial Decay) • Added additional supported DNN architectures to the Image Classifier o Inception v3 o ResNet 101 v2 o ResNet 50 v2 o MobileNet v2 https://devblogs.microsoft.com/dotnet/announcing-ml-net-1-4-global-availability-machine-learning-for-net/ Updated Model Builder in Visual Studio Use latest engine and includes new features (visual experience for local Image Classification model training)
- 37. ML.NET v1.4 (announced at Microsoft Ignite) Database Loader Load data from databases into the IDataView →model training directly against relational databases PredictionEnginePool for scalable deployments Optimization when deploy an ML model into multithreaded and scalable .NET Core web applications and services https://devblogs.microsoft.com/dotnet/announcing-ml-net-1-4-global-availability-machine-learning-for-net/ Enhanced for .NET Core 3.0 Take advantage of the new features when running in a .NET Core 3.0 application Use ML.NET in Jupyter notebooks .NET Support in Jupyter notebooks
- 41. About NimbusML • NimbusML provides state-of-the-art ML algorithms, transforms and components, aiming to make them useful for all developers, data scientists, and information workers and helpful in all products, services and devices. • The components are authored by the team members, as well as numerous contributors from MSR, CISL, Bing and other teams at Microsoft. • nimbusml is interoperable with scikit-learn estimators and transforms, while adding a suite of highly optimized algorithms written in C++ and C# for speed and performance.
- 42. NimbusML Features NimbusML trainers and transforms support the following data structures for the fit() and transform() methods: • numpy.ndarray • scipy.sparse_cst • pandas.DataFrame. NimbusML also supports streaming from files without loading the dataset into memory, which allows training on data significantly exceeding memory using FileDataStream. • With FileDataStream, NimbusML is able to handle up to billion features and billions of training examples for select algorithms
- 44. Thank You!
- 45. Questions?
- 46. Clemente Giorio R&D Senior Software Engineer @ Deltatre ▪ Augmented/Mixed/Virtual Reality ▪ Artificial Intelligence, Machine Learning ▪ Internet of Things ▪ Embedded Apps ▪ Multimodal Tracking @tinux80 Author About us
- 47. About us Ing. Gianni ROSA GALLINA R&D Senior Software Engineer @ Deltatre ▪ AI, Machine Learning, Deep Learning on multimedia content ▪ Virtual/Augmented/Mixed Reality ▪ Immersive video streaming & 3D graphics for sport events ▪ Cloud solutions, web backends, serverless, video workflows ▪ Mobile apps dev (Windows / Android / Xamarin) ▪ End-to-end solutions with Microsoft Azure @giannirg http://gianni.rosagallina.com Author