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The machine learning (ML) pipeline involves a complex relationship between the data, the model, and its implementation—each with its own risks that can adversely affect the utility and profitability of the solution. This course is a primer on what these risks are, where they come from, and how to mitigate them effectively.

In this course, you’ll start with a comprehensive look at the data side of the pipeline, including data privacy, data drift, and more. You’ll learn how to mitigate these in theory and practice. You’ll also discover problems related to ML models such as bias, security, and adversarial attacks. Finally, you’ll learn some of the alternative AI paradigms that exist in the world today—from causal AI to federated learning to generative AI.

A deep understanding of where problems can arise is a critical part of a data engineer or data scientist’s ML knowledge. From a career perspective, this course’s content can effectively address the real risks faced by developers while setting up ML pipelines.

Mitigating Disasters in ML Pipelines

What is _not_ a common step in the ML pipeline?

What is not a common step in the ML pipeline?

Which source of harm best represents the following scenario:

A model is built to predict voter turnout, but is constructed from a dataset including only voters from the Northeast.

Which source of harm best represents the following scenario:
A model is built to predict voter turnout, but is constructed from a dataset including only voters from the Northeast.

To a data scientist, deployment marks the end of the project. Monitoring and constant validation are often seen as unnecessary overhead.

There's no robust, reliable technological method of tracking a model after deployment.

The kind of bias that occurs after deployment is always extremely subtle and can't be detected by normal methods.

Why is deployment bias the most difficult to catch?

Why is deployment bias the most difficult to catch?

Sources of disasters and the ML pipeline

Sources of disasters and the ML pipeline

Test your knowledge of how disasters can infect an ML pipeline.

Introduction

Disasters in Data

Disasters in Models

Alternatives to Traditional ML

Conclusion

Assessment: Disasters in ML Pipelines

Quiz: ML Pipeline and Sources of Disasters