Dodge costly and time-consuming infrastructure tasks, and rapidly bring your machine learning models to production with MLOps and pre-built serverless tools!
In MLOps Engineering at Scale you will learn:
- Extracting, transforming, and loading datasets
- Querying datasets with SQL
- Understanding automatic differentiation in PyTorch
- Deploying model training pipelines as a service endpoint
- Monitoring and managing your pipeline’s life cycle
- Measuring performance improvements
MLOps Engineering at Scale shows you how to put machine learning into production efficiently by using pre-built services from AWS and other cloud vendors. You’ll learn how to rapidly create flexible and scalable machine learning systems without laboring over time-consuming operational tasks or taking on the costly overhead of physical hardware. Following a real-world use case for calculating taxi fares, you will engineer an MLOps pipeline for a PyTorch model using AWS server-less capabilities.
