Mitigating Disasters in ML Pipelines

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35 Lessons

Certificate of Completion

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

1 Assessment

16 Playgrounds

This course includes

1 Assessment

16 Playgrounds

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

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

What You'll Learn

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

Course Content

Introduction

Introduction The Standard ML Pipeline

Sources of Disasters Quiz: ML Pipeline and Sources of Disasters

Disasters in 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

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

Federated Learning Causal AI Online Learning Neurosymbolic AI Generative AI Quiz: Alternatives to Traditional ML

Conclusion

Final Thoughts

Assessment: Disasters in ML Pipelines

Assessment

Course Author

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Anthony Walker

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Emma Bostian 🐞

@EmmaBostian

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

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