Dec19 Meetup: ML & AI on Azure by Marc Schöni

Learn about the solution components available through the Azure cloud for building cutting edge ML & AI solutions at scale.

About Marc Schöni:
Marc is a Cloud Solution Architect at Microsoft with a wealth of experience for data science solutions across industries. On a daily basis he works with customers on designing real-world Azure based ML & AI solutions. Marc holds a Master’s degree in Data Science and is a lecturer on ML at University of St. Gallen.

You can find him at:
https://www.linkedin.com/in/aiguy

Transcript

1. A (quick) introduction
2. AI apps & agents Knowledge mining Machine learning
3. AI apps & agents Knowledge mining Machine learning
4. Azure Bot Service Azure Cognitive Services
5. Language Vision Speech Decision Web search Conversation transcription capability Language Understanding Ink Recognizer Personalizer Computer Vision Neural Text-to-Speech Anomaly Detector Speech Service Device SDK QnA Maker Most comprehensive list of cognitive capabilities
6. The most comprehensive pre-trained AI Language Vision Speech Decision Web search Bing Spell Check Custom Vision Personalizer Form Recognizer Neural Text-to-Speech Anomaly Detector Content Moderator Content Moderator Custom Speech Speech transcription Text-to-Speech Conversation transcription capability Face Video Indexer Ink Recognizer Computer Vision Language Understanding QnA Maker Text Analytics Translator Text Bing Web Search Bing Custom Search Bing Video Search Bing Image Search Bing Local Business Search Bing Visual Search Bing Entity Search Bing News Search Bing Autosuggest
7. Deploy anywhere using containers Azure Cognitive Services Wherever your data resides Vision Speech Language Decision
8. 4K Community commits 2M+ SDK downloads Microsoft Bot Framework Easily deploy and manage Build with open source tools Azure Bot Service 35K Active bots 3K Bots created weekly
9. Robotic process automation Call center automation Customer engagement Intelligent agents
10. DEMO Microsoft Healthbot http://aka.ms/healthbot
11. AI apps & agents Knowledge mining Machine learning
12. AI apps & agents Knowledge mining Machine learning
13. Form Recognizer Azure Cognitive Search Text Analytics Speech to Text Azure Machine Learning (custom skills) …
14. Uncover latent insights from all your content
15. Ingest Extract experience 2 3 1 document primaryImage secondaryImage job company duration job company duration job company duration
16. Cognitive search capability Ingest Candidates with 5 years experience Extract Search experience 2 3 1 document primaryImage secondaryImage job company duration job company duration job company duration
17. Attach search to an app Expand to content understanding
18. DEMO JFK Files http://aka.ms/jfkfiles-demo
19. Quickly extract text, tables and key value pairs with simple REST API calls Start with any type of forms Preview W-2 Receipt New
20. Microsoft Confidential
21. AI apps & agents Knowledge mining Machine learning
22. AI apps & agents Knowledge mining Machine learning
23. Domain specific pretrained models To simplify solution development Popular frameworks To build advanced deep learning solutions Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Familiar data science tools To simplify model development From the Intelligent Cloud to the Intelligent Edge Azure Databricks Machine Learning VMs TensorFlow PyTorch ONNX Language Speech … Decision Vision Scikit-Learn Azure Notebooks Jupyter Visual Studio Code Command line Azure Machine Learning CPU GPU FPGA Web search
24. Azure Machine Learning
25. Azure Machine Learning Any tool + any framework Automated ML + drag & drop + code first For all skill levels Open Integrated with Azure DevOps Industry leading MLOps
26. Automated machine learning UI Visual interface Machine learning notebooks New capabilities in Azure Machine Learning service Centralized model registry
27. Automated machine learning
28. How much is this car worth? Automated machine learning
29. 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
30. Which algorithm? Which parameters? Which features? Mileage Condition Car brand Year of make Regulations … Gradient Boosted Nearest Neighbors SGD Bayesian Regression LGBM … Nearest Neighbors Criterion Loss Min Samples Split Min Samples Leaf XYZ Model Iterate Gradient Boosted N Neighbors Weights Metric P ZYX Mileage Car brand Year of make Car brand Year of make Condition Track
31. Track Which algorithm? Which parameters? Which features? Iterate
32. Enter data Define goals Apply constraints Output Input Intelligently test multiple models in parallel Optimized model 95%
33. Feature Importance Mileage Condition Car brand Year of make Regulations 0 1 Feature Importance Mileage 0 1 Condition Car brand Year of make Regulations 95% model 70% model Enable model explain-ability for every automated ML iteration, not just the optimal model
34. Azure Machine Learning pipelines
35. 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
36. 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
37. 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 testing Model validation Deployment Batch scoring Error resolved Testing error
38. 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
39. 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
40. New offerings and integrations ONNX Runtime updates Native MLflow support Azure Open Datasets
41. Build advanced deep learning solutions Use your favorite deep 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
42. Simplify deployment + accelerate inferencing with ONNX runtime Track models in production Capture model telemetry From the Intelligent Cloud to the Intelligent Edge On-premises Cloud
43. DEMO Azure Machine Learning http://ml.azure.com