Book Content
chapters • 14h32m total length
1. Building an End-to-end Machine Learning Pipeline
2. Choosing a Machine Learning Service in Azure
3. Data Experimentation and Visualization using Azure
4. ETL, Data Preparation and Feature Extraction
5. Advanced Feature Extraction with NLP
6. Building ML Models using Azure Machine Learning
7. Training Deep Neural Networks on Azure
8. Hyperparameter Tuning and Automated Machine Learning
9. Distributed Machine Learning on Azure ML Clusters
10. Building a Recommendation Engine in Azure
11. Deploying and Operating Machine Learning Models
12. MLOps – DevOps for Machine Learning
13. What's next?
