Book Content
chapters • 20h48m total length
1. Understanding the End-to-End Machine Learning Process
2. Choosing the Right Machine Learning Service in Azure
3. Preparing the Azure Machine Learning Workspace
4. Ingesting Data and Managing Datasets
5. Performing Data Analysis and Visualization
6. Feature Engineering and Labeling
7. Advanced Feature Extraction with NLP
8. Azure Machine Learning Pipelines
9. Building ML Models Using Azure Machine Learning
10. Training Deep Neural Networks on Azure
11. Hyperparameter Tuning and Automated Machine Learning
12. Distributed Machine Learning on Azure
13. Building a Recommendation Engine in Azure
14. Model Deployment, Endpoints, and Operations
15. Model Interoperability, Hardware Optimization, and Integrations
16. Bringing Models into Production with MLOps
17. Preparing for a Successful ML Journey
