Section 4: Optimization and Deployment of Machine Learning Models
by Christoph Korner,
Kaijisse Waaijer
Mastering Azure Machine Learning
- Preface
- Section 1: Azure Machine Learning
- 1. Building an end-to-end machine learning pipeline in Azure
- 2. Choosing a machine learning service in Azure
- Section 2: Experimentation and Data Preparation
- 3. Data experimentation and visualization using Azure
- 4. ETL, data preparation, and feature extraction
- 5. Azure Machine Learning pipelines
- 6. Advanced feature extraction with NLP
- Section 3: Training Machine Learning Models
- 7. Building ML models using Azure Machine Learning
- 8. Training deep neural networks on Azure
- 9. Hyperparameter tuning and Automated Machine Learning
- 10. Distributed machine learning on Azure
- 11. Building a recommendation engine in Azure
- Section 4: Optimization and Deployment of Machine Learning Models
- 12. Deploying and operating machine learning models
- 13. MLOps—DevOps for machine learning
- 14. What's next?
- Index
After loading, preprocessing, and training models, in this section, the reader will now learn how to deploy models for batch and online scoring. The reader will automate the deployment to Azure Databricks and Azure Kubernetes Service using Azure Machine Learning.
This section comprises the following chapters:
- Chapter 12, Deploying and operating machine learning models
- Chapter 13, MLOps—DevOps for machine learning
- Chapter 14, What’s next?
-
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