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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.

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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.

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

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

TAKEAWAY SKILLS

Machine Learning

Data Science

Data Pipeline Engineering

Natural Language Processing

Content

35 Lessons4 Quizzes1 Assessment

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

Measuring Causal Relations with Python

Project

Premium

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

Adversarial Robustness of Neural Networks

Project

Premium

Conclusion

1 Lessons

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.

Course Author:

Abhinav Raghunathan

Developed by MAANG Engineers

Every Educative lesson is designed by a team of ex-MAANG software engineers and PhD computer science educators, and developed in consultation with developers and data scientists working at Meta, Google, and more. Our mission is to get you hands-on with the necessary skills to stay ahead in a constantly changing industry. No video, no fluff. Just interactive, project-based learning with personalized feedback that adapts to your goals and experience.

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