Welcome to Production ML systems, the first course in the advanced machine learning on Google Cloud Specialization.

This course focuses on building production machine learning models and the considerations behind them.

We’ll be covering what ML architectures are composed of, and the why and how of making good systems design decisions.

Real-world production ML systems are large ecosystems, of which the model code is just a small part.

The rest consists of code that performs critical functions, like data extraction, feature engineering, monitoring, and a serving infrastructure.

This course is devoted to exploring the characteristics that make for a robust ML system beyond its ability to make good predictions.

In the first module, Architecting Production ML Systems, we’ll explore what an ML system should be able to do and the components that take responsibility for those actions.

In Module 2, Designing Adaptable ML Systems, you’ll see how change can affect an ML system and what can be done to mitigate those effects.

In Module 3, Designing High-Performance ML Systems, we’ll explore how to optimize the performance of an ML system by choosing the right hardware and removing bottlenecks.

Finally, in Module 4, Building Hybrid ML Systems, you’ll learn about the technology behind hybrid systems that allows you to run your workloads on the Cloud, on the edge using mobile devices, or on premises.