2020 10 22 AI Fundamentals - Azure Machine Learning
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Slides used for the "Azure Machine Learning" session with the ConoSurTech team. Covering topics Intro to Machine Learning, MLOps, AutoML and more.
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2020 10 22 AI Fundamentals - Azure Machine Learning
- 1. Uso automatizado de Machine Learning en Azure Machine Learning @CONOSUR.TECH
- 2. @CONOSUR.TECH Uso automatizado de Machine Learning en Azure Machine Learning
- 3. @CONOSUR.TECH • Azure Machine Learning • Tutorial: Predict automobile price with the designer • Tutorial: Deploy a machine learning model with the designer • Tutorial: Get started with Azure Machine Learning in your development environment • How Azure Machine Learning works: Architecture and concepts • MLOps with Azure ML • Azure DataBricks, Notebooks
- 5. @CONOSUR.TECH ¿QUIÉN SOY? Bruno Capuano Innovation Lead Microsoft MVP AI https://www.linkedin.com/in/elbruno/ @elbruno
- 7. @CONOSUR.TECH SQL DB Cosmos DB Datawarehouse Data lake Blob storage … Prepare Data Build & Train Deploy Machine Learning Process
- 8. @CONOSUR.TECH Comment Toxic? (Sentiment) ==RUDE== Dude, you are rude … 1 == OK! == IM GOING TO VANDALIZE … 1 I also found use of the word "humanists” confusing … 0 Oooooh thank you Mr. DietLime … 0 Wikipedia detox data at https://figshare.com/articles/Wikipedia_Talk_Labels_Personal_Attacks/4054689 Features (input) Label (output) Sentiment Analysis
- 9. @CONOSUR.TECH Is this A or B? Is this a toxic comment? Yes or no Sentiment analysis explained
- 10. Example Comment Toxic? (Sentiment) ==RUDE== Dude, you are rude … 1 == OK! == IM GOING TO VANDALIZE … 1 I also found use of the word "humanists” confusing … 0 Oooooh thank you Mr. DietLime … 0 Important concepts: Data Prepare Your Data
- 11. Prepare Your Data 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 ==RUDE== Dude, you are rude … == OK! == IM GOING TO VANDALIZE … I also found use of the word "humanists” … Oooooh thank you Mr. DietLime … Important concepts: Transformer
- 12. Build & Train Example Estimator Comment Toxic? (Sentiment) ==RUDE== Dude, you … 1 == OK! == IM GOING … 1 I also found use of the … 0 Oooooh thank you Mr. … 0 Important concepts: Trainer
- 13. Comment ==RUDE== Dude, you … Prediction Function Predicted Label – Toxic? (Sentiment) 1 Run Example Important concepts: Model
- 14. Azure ML Hello World ! MakeMagicHappen(); https://www.avanade.com/AI
- 16. @CONOSUR.TECH Machine Learning on Azure Domain Specific Pretrained Models To reduce time to market Azure Databricks Machine Learning VMs Popular Frameworks To build machine learning and deep learning solutions TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Productive Services To empower data science and development teams Powerful Hardware To accelerate deep learning Scikit-Learn PyCharm Jupyter Familiar Data Science Tools To simplify model development Visual Studio Code Command line CPU GPU FPGA From the Intelligent Cloud to the Intelligent Edge@CONOSUR.TECH
- 17. @CONOSUR.TECH Building blocks for a Data Science Project Data sources Classical ML Deep learning Build and train models Experimentation and pipelines Hyperparameter tuning DevOps for data scienceDeployment
- 18. @CONOSUR.TECH Azure ML Automated Machine Learning
- 19. @CONOSUR.TECH How much is the taxi fare for 1 passenger going from Burlington to Toronto? Automated Machine Learning
- 20. @CONOSUR.TECH Criterion Loss Min Samples Split Min Samples Leaf XYZ Parameter 1 Parameter 2 Parameter 3 Parameter 4 … Distance Trip time Car type Passengers Time of day … Gradient Boosted Nearest Neighbors SGD Bayesian Regression LGBM … Distance Gradient Boosted Model Car type Passengers Getting started w/machine learning can be hard Which algorithm? Which parameters?Which features? Getting started with ML can be hard
- 21. @CONOSUR.TECH N Neighbors Weights Metric P ZYX Criterion Loss Min Samples Split Min Samples Leaf XYZ Which algorithm? Which parameters?Which features? Distance Trip time Car type Passengers Time of day … Gradient Boosted Nearest Neighbors SGD Bayesian Regression LGBM … Nearest Neighbors Model Iterate Gradient BoostedDistance Car brand Year of make Car type Passengers Trip time Getting started w/machine learning can be hardGetting started with ML can be hard
- 22. @CONOSUR.TECH Which algorithm? Which parameters?Which features? Iterate Getting started w/machine learning can be hardGetting started with ML can be hard
- 23. @CONOSUR.TECH 25%40%70% 25% 95% 25% 25% 25% 25% 40% 40% 40% 40% 70% 70% 70%Enter data Define goals Apply constraints Input Intelligently test multiple models in parallel Optimized model 95% AutoML accelerates model development
- 24. @CONOSUR.TECH 70%95% Feature importance Distance Trip time Car type Passengers Time of day 0 1 Model B (70%) Distance 0 1 Trip time Car type Passengers Time of day Feature importance Model A (95%) ML.NET accelerates model development with model explainability AutoML accelerates model development
- 25. @CONOSUR.TECH DevOps for Data Science MLOps
- 26. @CONOSUR.TECH Prepare Data Register and Manage Model Train & Test Model Build Image … Build model (your favorite IDE) Deploy Service Monitor Model Prepare Experiment Deploy DevOps loop for data science
- 27. @CONOSUR.TECH DevOps loop for data science 01010100101001011101101001001001011 01010010001000111101001010101001010 01010100101001011101101001001001011 01010010001000111101001010101001010 00101001011101101001001001011010100 10001000111101001010101001010010101 00101001011101101001 Prepare Data Prepare Register and Manage Model Build Image … Build model (your favorite IDE) Deploy Service Monitor Model Train & Test Model
- 28. @CONOSUR.TECH Model Management in detail Create/Retrain Model Enable DevOps with full CI/CD integration with VSTS Register Model Track model versions with a central model registry Monitor Oversea deployments through Azure AppInsights
- 29. @CONOSUR.TECH Experimentation Use leaderboards, side by side run comparison and model selection Conduct a hyperparameter search on traditional ML or DNN Leverage service-side capture of run metrics, output logs and models Manage training jobs locally, scaled-up or scaled-out 95 % 80% 75% 90% 85%
- 30. @CONOSUR.TECH Azure Machine Learning Pipelines
- 31. @CONOSUR.TECH Azure Machine Learning Pipelines Prepare data Build & train models Deploy & predict Data storage locations Data ingestion Data Preparation Model building & training Model deployment Normalization Transformation Validation Featurization Hyper-parameter tuning Automatic model selection Model testing Model validation Deployment Batch scoring
- 32. @CONOSUR.TECH Prepare data Build & train models Deploy & predict Data storage locations Data ingestion Data Preparation Model building & training Model deployment Normalization Transformation Validation Featurization Hyper-parameter tuning Automatic model selection Model testing Model validation Deployment Batch scoring Normalization Transformation Validation Featurization Hyper-parameter tuning Automatic model selection Model testing Testing error Azure Machine Learning Pipelines
- 33. @CONOSUR.TECH Prepare data Build & train models Deploy & predict Data storage locations Data ingestion Data Preparation Model building & training Model deployment Normalization Transformation Validation Featurization Hyper-parameter tuning Automatic model selection Model testing Model validation Deployment Batch scoring Normalization Transformation Validation Featurization Hyper-parameter tuning Automatic model selection Model testing Testing errorModel testing Model validation Deployment Batch scoring Error resolved Azure Machine Learning Pipelines
- 34. @CONOSUR.TECH Azure Machine Learning Pipelines with new data Prepare data Build & train models Deploy & predict Data storage locations Data ingestion Data Preparation Model building & training Model deployment Normalization Transformation Validation Featurization Hyper-parameter tuning Automatic model selection Model testing Model validation Deployment Batch scoring Normalization Transformation Validation Featurization Hyper-parameter tuning Automatic model selection Model testing Model validation New data Deployment Batch scoring
- 35. @CONOSUR.TECH Advantages of Azure ML Pipelines Unattended runs Schedule a few steps to run in parallel or in sequence to focus on other tasks while your pipeline runs Mixed and diverse compute Use multiple pipelines that are reliably coordinated across heterogeneous and scalable computes and storages Reusability Create templates of pipelines for specific scenarios such as retraining and batch scoring Tracking and versioning Name and version your data sources, inputs and outputs with the pipelines SDK
- 36. @CONOSUR.TECH Support for Open source frameworks
- 37. @CONOSUR.TECH Popular Frameworks Use your favorite machine learning frameworks without getting locked into one framework ONNX Community project created by Facebook and Microsoft Use the best tool for the job. Train in one framework and transfer to another for inference TensorFlow PyTorch Scikit-Learn MXNet Chainer Keras
- 38. Frameworks Create Native support Converters Services Azure Custom Vision Service Native support Other Devices (iOS, etc) ML.NE T Azure Windows Server 2019 VM Azure Machine Learning services Ubuntu VM Deploy Windows Devices ONNX Model Native support Converters
- 40. @CONOSUR.TECH Flexible Deployment Deploy and manage models on intelligent cloud and edge Train & deploy Train & deploy Deploy Model optimization for cloud & edge Manage models in production Capture model telemetry Retrain models
- 41. @CONOSUR.TECH Deploy Azure ML models at scale Azure Machine Learning Service Azure Machine Learning Experimentation Cognitive Services External Model Model Registry Register Model Cloud Service Heavy Edge Light Edge Image Registry Create & Register Image Your IDE Scoring File Image Registry Deployment & Model Monitoring
- 42. @CONOSUR.TECH Deployments to Compute Targets