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Mitigating Disasters in ML Pipelines

Gain insights into managing risks in ML pipelines by exploring data issues, model biases, and security threats. Learn about data privacy, adversarial attacks, and alternative AI paradigms like causal AI and federated learning.

Advanced

35 Lessons

Certificate of Completion

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Explanations

AI-POWERED

Explanations

This course includes

1 Assessment

16 Playgrounds

Course Overview

What You'll Learn

Course Content

Recommendations

Course Overview

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 pr...Show More

The machine learning (ML) pipeline involves a complex relationship between the data, the model, and its implementation—each with...Show More

TAKEAWAY SKILLS

Machine Learning

Data Science

Data Pipeline Engineering

Natural Language Processing

What You'll Learn

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

The ability to understand, identify, and fix potential problems with machine learning (ML) pipelines

Course Content

Introduction

4 Lessons

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

12 Lessons

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.

Understanding Model Bias

Large Language Models

Post-Training Model Bias Mitigation

In-Training Model Bias Mitigation

Adversarial Attacks

Thwarting Adversarial Attacks

Other Model Attacks

Intro to Model Explainability

Explainability Methods

Adversarial Attacks on Explanations

Case Study: Chest X-rays

Quiz: Disasters in Models

Alternatives to Traditional ML

6 Lessons

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

Quiz: Alternatives to Traditional ML

Conclusion

1 Lessons

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

Final Thoughts

Assessment: Disasters in ML Pipelines

Assessment

Course Author

Abhinav Raghunathan

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