Course curriculum

    1. Data Validation in Machine Learning Pipelines

    2. Exploring and Validating Weather Data

    3. Continual Retraining for Improved Model Performance

    4. Understanding and Detecting Data Corruptions

    1. Introducing Schema Validation Technique

    2. Using TFX and Schema Validation

    1. Detecting Data Skew using TensorFlow Data Validation

    1. Drift Detection and Data Validation using GATE (Part 1)

    2. Drift Detection and Data Validation using GATE (Part 2)

    1. Testing Your Knowledge

    2. Final Report

    1. Extra Thoughts: Unstructured Data and Observability

    2. Course Recap and Future Directions

    3. More resources for you

About this course

  • Free
  • 14 lessons
  • 1.5 hours of video content

Sign up for this free Weights & Biases course to:

  • Grasp data validation importance

    Discover how data validation enhances machine learning pipelines, by managing data drift, schema validation, and handling data corruption.

  • Dive into hands-on examples

    Analyze real-world datasets with techniques such as schema validation, drift detection, and continual retraining.

  • Utilize powerful tools

    Learn to use TensorFlow Data Validation (TFDV) and the GATE method to effectively detect data drifts and maintain data quality.

Prerequisites

  • Basic knowledge of machine learning

  • Familiarity with Python programming

Instructor

Shreya Shankar

PhD student at UC Berkeley

Shreya Shankar is doing her PhD in databases at UC Berkeley. She is broadly interested in data management for machine learning (ML), with an emphasis on helping non-ML experts build and productionize ML pipelines. She is currently working on a new framework for building ML pipelines with automatic data validation, model retraining, and observability. Outside of research, she enjoys making ice creams, hiking, and sampling Bay Area coffee roasters.

Course Reviews

Hear from Data Validation in Production Pipelines Certification takers

5 star rating

Preventing Data Drifts in Production ML: Insights & Techniques

NAVEEN R

I got wonderful insights on predicting and preventing data drifts in production ML Pipeline with real world use cases and dataset. Tracking the ML Pipeline with weights and biases was interesting to learn and generate the report. Thank you

I got wonderful insights on predicting and preventing data drifts in production ML Pipeline with real world use cases and dataset. Tracking the ML Pipeline with weights and biases was interesting to learn and generate the report. Thank you

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