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

سرفصل های دوره

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.

01 - Introduction

01 - A different approach to AI

02 - Overview of the course objectives and agenda

02 - 1. What Is Data-Centric AI

01 - Introduction to data-centric AI

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

03 - Data quality and reliability in AI applications

03 - 2. Why Is Data-Centric AI Important

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

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

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

04 - 3. Workflow of Data-Centric AI

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

02 - Deep dive into data-centric AI components

03 - Iterative nature of the workflow for ML applications

05 - 4. MLOps Introduction and Importance

01 - Purpose of MLOps (Machine Learning Operations)

02 - Challenges faced in deploying and maintaining ML models

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

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

02 - Data personas in MLOps workflow

03 - Optimizing the MLOps process Development

04 - Optimizing the MLOps process Productionalizing

07 - 6. Data-Centric AI in Action

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

02 - Best practices

03 - Code example Exploration

08 - 7. Explainability and Interpretability

01 - Importance of model explainability and interpretability

02 - Techniques for understanding and interpreting ML models

03 - Code example Model validation

09 - 8. Bias Detection and Mitigation

01 - Discussion on the challenges of bias in AI systems

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

03 - Code example Bias detection and mitigation

10 - 9. Data Drift and Model Drift

01 - Monitoring and maintaining ML models in production

02 - Understanding data drift and model drift

11 - 10. Responsible AI

01 - Introduction to ethical considerations in AI

02 - Principles for responsible AI development and deployment

12 - Conclusion

01 - Closing remarks and next steps for further learning

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تولید کننده: LinkedIn Learning (Lynda)