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Mitigating Disasters in ML Pipelines

Learn about ML pipeline risk management data, bias, and security. Explore data privacy, attacks, and AI alternatives like causal AI and federated learning.

5.0

35 Lessons

2 Projects

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LEARNING OBJECTIVES

The ability to understand, identify, and fix potential problems with machine learning (ML) pipelines
An understanding of issues in data and model privacy, as well as malicious attacks
A working knowledge of the dangers of large language models (LLMs)
An understanding of how to mitigate risks associated with ML pipelines

Learning Roadmap

35 Lessons4 Quizzes1 Assessment

Introduction

Get familiar with mitigating faults in ML pipelines, understanding biases, and ensuring data integrity.

Introduction

The Standard ML Pipeline

Sources of Disasters

Quiz: ML Pipeline and Sources of Disasters

Disasters in Data

Solve challenges with mitigating data and privacy biases, detecting drift, and safeguarding data.

Introduction to Data Bias

Diagnosing Data Bias

Theory of Data Bias Mitigation

Practice of Data Bias Mitigation

Case Study: COMPAS in US Justice System

Data Drift

Detecting Data Drift

Data Privacy

Reidentification Example

Theory of Ensuring Data Privacy

Ensuring Data Privacy in Practice

Quiz: Disasters in Data

Disasters in Models

12 Lessons

Examine model biases, adversarial vulnerabilities, explainability challenges, and mitigation strategies.

Measuring Causal Relations with Python

Project

Alternatives to Traditional ML

6 Lessons

Break down complex ideas in federated learning, causal AI, online learning, neurosymbolic AI, and generative AI.

Adversarial Robustness of Neural Networks

Project

Conclusion

Ensure safety and trust in evolving ML pipelines with vigilant governance and transparency.

Final Thoughts

Assessment: Disasters in ML Pipelines

Assessment

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Developed by MAANG Engineers

ABOUT THIS COURSE

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.

ABOUT THE AUTHOR

Abhinav Raghunathan

I'm an engineer and a builder at heart with tons of experience in machine learning, artificial intelligence, and data science specifically in the finance domain. In the past, I have worked with institutions like Point72, Vanguard, IHS Markit, and Starry.

Learn more about Abhinav

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