Machine Learning Design Patterns

Jan 19, 10:30AM PDT(06:30PM GMT).
  • Free 441 Attendees
Design patterns capture best practices and solutions to recurring problems. Join us for the tech talks and Q&As, by the authors of the newly released O’Reilly book “Machine Learning Design Patterns”, covering solutions to common challenges in Data Preparation, Model Building, and MLOps.

Lak, Sara and Michael will introduce three of these tried-and-proven methods to help engineers tackle problems that frequently crop up during the ML process. They will dive into the pattern: a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation.

Talk #1: Data Representation Design Pattern: Embeddings, by Valliappa Lakshmanan
Talk #2: Reproducibility Design Pattern: Model Versioning, by Sara Robinson
Talk #3: Problem Representation Design Pattern: Multilabel, by Michael Munn

We will raffle for the 5 copies of the book (Link) (for the 5 winners: hard copy ship to you if you are within USA; e-copy if you are outside of USA).

Lak,Sara,Michael (Google)

Global Head for Data Analytics and AI Solutions on Google Cloud. His team builds software solutions for business problems using Google Cloud data analytics and machine learning products

Sara Robinson
Developer advocate on the Google Cloud team focusing on ML. She inspires developers and data scientists to integrate ML through demos, online content, and events

Michael Munn
ML solutions engineer in Google Cloud. He helps customers design, implement, and deploy machine learning models and teaches the ML Immersion Program in Google Advanced Solutions Lab

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