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
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TAKEAWAY SKILLS
Content
1.
Introduction
4 Lessons
Get familiar with mitigating faults in ML pipelines, understanding biases, and ensuring data integrity.
2.
Disasters in Data
12 Lessons
Solve challenges with mitigating data and privacy biases, detecting drift, and safeguarding data.
3.
Disasters in Models
12 Lessons
Examine model biases, adversarial vulnerabilities, explainability challenges, and mitigation strategies.
4.
Alternatives to Traditional ML
6 Lessons
Break down complex ideas in federated learning, causal AI, online learning, neurosymbolic AI, and generative AI.
5.
Conclusion
1 Lessons
Ensure safety and trust in evolving ML pipelines with vigilant governance and transparency.
Certificate of Completion
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Course Author:
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