Data-Centric AI: Best Practices, Responsible AI, and More

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Aishwarya Srinivasan

2:50:14

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01 - Introduction

01 - A different approach to AI.mp4

00:49

02 - Overview of the course objectives and agenda.mp4

01:00

02 - 1. What Is Data-Centric AI

01 - Introduction to data-centric AI.mp4

04:33

02 - Understanding the role of data in AI and machine learning.mp4

04:42

03 - Data quality and reliability in AI applications.mp4

03:26

03 - 2. Why Is Data-Centric AI Important

01 - Significance of data-centric AI in real-world scenarios.mp4

06:24

02 - Benefits of adopting a data-centric approach in AI projects.mp4

02:43

03 - Case studies highlighting the impact of data-centric AI.mp4

06:43

04 - 3. Workflow of Data-Centric AI

01 - End-to-end workflow of data-centric AI.mp4

05:34

02 - Deep dive into data-centric AI components.mp4

05:13

03 - Iterative nature of the workflow for ML applications.mp4

01:47

05 - 4. MLOps Introduction and Importance

01 - Purpose of MLOps (Machine Learning Operations).mp4

06:04

02 - Challenges faced in deploying and maintaining ML models.mp4

04:14

06 - 5. Building Optimized MLOps with Data-Centric AI

01 - Adding data-centric AI principles into the MLOps workflow.mp4

05:08

02 - Data personas in MLOps workflow.mp4

05:11

03 - Optimizing the MLOps process Development.mp4

10:02

04 - Optimizing the MLOps process Productionalizing.mp4

08:28

07 - 6. Data-Centric AI in Action

01 - Data validation, train-test validation, and model validation.mp4

03:15

02 - Best practices.mp4

20:59

03 - Code example Exploration.mp4

07:36

08 - 7. Explainability and Interpretability

01 - Importance of model explainability and interpretability.mp4

03:29

02 - Techniques for understanding and interpreting ML models.mp4

03:58

03 - Code example Model validation.mp4

03:55

09 - 8. Bias Detection and Mitigation

01 - Discussion on the challenges of bias in AI systems.mp4

04:22

02 - Detecting and mitigating bias in data-centric AI projects.mp4

04:28

03 - Code example Bias detection and mitigation.mp4

04:54

10 - 9. Data Drift and Model Drift

01 - Monitoring and maintaining ML models in production.mp4

08:41

02 - Understanding data drift and model drift.mp4

08:47

11 - 10. Responsible AI

01 - Introduction to ethical considerations in AI.mp4

05:02

02 - Principles for responsible AI development and deployment.mp4

05:47

12 - Conclusion

01 - Closing remarks and next steps for further learning.mp4

03:00

Description

Machine learning typically focuses on producing effective models for a given dataset. In real-world applications, data is messy and improving models is not the only way to get better performance. Data-centric AI (DCAI) is an emerging science that studies techniques to improve datasets, which is often the best way to improve performance in practical ML applications. While data scientists have long practiced this manually via ad hoc trial/error and intuition, DCAI considers the improvement of data as a systematic engineering discipline. In this course, Aishwarya Srinivasan covers the data-centric principles that guide our path forward in this new age of AI as we shift from a model-centric approach to a data-centric paradigm. Learn about DCAI—what it is and the value it offers. Aishwarya covers the DCAI workflow; MLOps as part of DCAI; data validation and preprocessing; model validation; bias detection and mitigation; responsible AI; and more.

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Data Science

Artificial Intelligence

Aishwarya Srinivasan

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Linkedin Learning

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LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.