Closing remarks and next steps for further learning

Contents

• Introduction Introduction

  • A different approach to AI

    49s
  • (Locked)
    Overview of the course objectives and agenda

    1m
• 1. What Is Data-Centric AI? 1. What Is Data-Centric AI?

  • Introduction to data-centric AI

    4m 33s
  • (Locked)
    Understanding the role of data in AI and machine learning

    4m 42s
  • (Locked)
    Data quality and reliability in AI applications

    3m 26s
• 2. Why Is Data-Centric AI Important? 2. Why Is Data-Centric AI Important?

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

    6m 24s
  • (Locked)
    Benefits of adopting a data-centric approach in AI projects

    2m 43s
  • (Locked)
    Case studies highlighting the impact of data-centric AI

    6m 43s
• 3. Workflow of Data-Centric AI 3. Workflow of Data-Centric AI

  • (Locked)
    End-to-end workflow of data-centric AI

    5m 34s
  • (Locked)
    Deep dive into data-centric AI components

    5m 13s
  • (Locked)
    Iterative nature of the workflow for ML applications

    1m 47s
• 4. MLOps: Introduction and Importance 4. MLOps: Introduction and Importance

  • (Locked)
    Purpose of MLOps (Machine Learning Operations)

    6m 4s
  • (Locked)
    Challenges faced in deploying and maintaining ML models

    4m 14s
• 5. Building Optimized MLOps with Data-Centric AI 5. Building Optimized MLOps with Data-Centric AI

  • Adding data-centric AI principles into the MLOps workflow

    5m 8s
  • (Locked)
    Data personas in MLOps workflow

    5m 11s
  • (Locked)
    Optimizing the MLOps process: Development

    10m 2s
  • (Locked)
    Optimizing the MLOps process: Productionalizing

    8m 28s
• 6. Data-Centric AI in Action 6. Data-Centric AI in Action

  • (Locked)
    Data validation, train-test validation, and model validation

    3m 15s
  • (Locked)
    Best practices

    20m 59s
  • (Locked)
    Code example: Exploration

    7m 36s
• 7. Explainability and Interpretability 7. Explainability and Interpretability

  • (Locked)
    Importance of model explainability and interpretability

    3m 29s
  • (Locked)
    Techniques for understanding and interpreting ML models

    3m 58s
  • (Locked)
    Code example: Model validation

    3m 55s
• 8. Bias Detection and Mitigation 8. Bias Detection and Mitigation

  • (Locked)
    Discussion on the challenges of bias in AI systems

    4m 22s
  • (Locked)
    Detecting and mitigating bias in data-centric AI projects

    4m 28s
  • (Locked)
    Code example: Bias detection and mitigation

    4m 54s
• 9. Data Drift and Model Drift 9. Data Drift and Model Drift

  • (Locked)
    Monitoring and maintaining ML models in production

    8m 41s
  • (Locked)
    Understanding data drift and model drift

    8m 47s
• 10. Responsible AI 10. Responsible AI

  • (Locked)
    Introduction to ethical considerations in AI

    5m 2s
  • (Locked)
    Principles for responsible AI development and deployment

    5m 47s
• Conclusion Conclusion

  • (Locked)
    Closing remarks and next steps for further learning

    3m