13. MLOps—DevOps for machine learning

In the previous chapter, we covered machine learning (ML) deployments in Azure using automated Azure Machine Learning deployments for real-time scoring services, Azure Pipelines for batch prediction services, and ONNX, FPGAs, and Azure IoT Edge for alternative deployment targets. If you have read all of the chapters preceding this one, you will have seen and implemented a complete end-to-end ML pipeline with data cleansing, preprocessing, labeling, experimentation, model development, training, optimization, and deployment.

Congratulations on making it this far! You now possess all the skills needed to connect the bits and pieces together for MLOps and to create DevOps pipelines for your ML models.

Throughout this book, we have emphasized how every step of the ML training and deployment process can be scripted through Bash, PowerShell, the Python SDK, or any other library wrapping the Azure Machine Learning REST service....