Azure Applied AI and AutoML

This is the presentation I used for a session at Microsoft Turkey Startup Bootcamp where we discussed Azure Applied AI Services and the use of AutoML.

Transcript

1. Azure Applied AI and AutoML Daron Yöndem Azure Application Development Lead for MEA Microsoft http://daron.me @daronyondem
2. Different flavors of AI/ML Control Productivity Machine Learning Supervised, Unsupervised, and reinforcement learning AutoML Automation for iterative tasks of ML AI Understand \ Interpret\ Learn\ and make decisions. Applied AI task-specific AI, and business logic as turnkey AI services. Azure ML Cognitive Services ML AI
3. Azure AI
4. What is Applied AI? AI models
5. What are Azure Applied AI Services? Extract actionable insights from your videos Proactively monitor metrics and diagnose issues Bring AI-powered cloud search to your mobile and web apps. Extract actionable insights from your videos Turn documents into usable data at a fraction of the time and cost Help users read and comprehend text
6. No Code Chatbot Demo
7. What is AutoML? • Automated Machine Learning (AutoML) is the process of automating end-to-end process of applying Machine Learning (ML) to create, develop and deploy predictive models so that any enterprise benefits from data. • Low-code / No-Code Experience • Keeps searching for the algorithm and hypermeters based on the metrics you define.
8. The Process SQL DB Cosmos DB Datawarehouse Data lake Blob storage … Prepare Data Build & Train Deploy
9. Sample Problem How much is this car worth?
10. Model creation takes time… a lot o time… Which algorithm? Which parameters? Which features? Mileage Condition Car brand Year of make Regulations … Gradient Boosted Nearest Neighbors SVM Bayesian Regression LGBM … Nearest Neighbors 50% Model Iterate 30% Gradient Boosted Mileage Car brand Year of make Car brand Year of make Condition Parameter 1 Parameter 2 Parameter 3 Parameter 4 …
11. The Complexity
12. Model Selection & Hyperparameter Tuning Dataset Training Algorithm 1 Hyperparameter Values – config 1 Model 1 Hyperparameter Values – config 2 Model 2 Hyperparameter Values – config 3 Model 3 Model Training Infrastructure Training Algorithm 2 Hyperparameter Values – config 4 Model 4 Repetitive & Manual
13. Introducing Automated Machine Learning Dataset Optimization Metric Constraints (Time/Cost) ML Model Automated ML Accessible & Faster
14. How to use AutoML in Azure? • Code: Azure Machine Learning Pyhton SDK • No-Code: Azure Machine Learning Studio
15. Different tasks • Classification: Fraud Detection, Marketing Prediction • Regression: Predict pricing • Time-series forecasting: Sales and Demand Forecasting
16. Data Scientist in a Box Demo
17. Thanks http://daron.me | @daronyondem Grab slides on http://decks.daron.me/