Machine Learning Design Patterns

Online event

RSVP on Eventbrite: http://bit.ly/machine-learning-design-patterns. Design patterns capture best practices and solutions to recurring problems. Join us for the great talks and AMA, 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

Nov 19, 2020, 1:00 – 2:30 AM

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About this event

RSVP on Eventbrite: http://bit.ly/machine-learning-design-patterns.

Design patterns capture best practices and solutions to recurring problems. Join us for the great talks and AMA, 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. And we will wrap up the event with AMA (Ask Me Anything).

There will be a Kahoot quiz with top 5 winners getting a free copy of the book.

AGENDA
* 5:00pm Talk by Lak
* 5:15pm Talk by Sara
* 5:30pm Talk by Michael
* 5:45pm AMA (Ask Me Anything)

TALKS

The Bridged Schema pattern, Valliappa Lakshmanan

When an input provider makes improvements to their data feed, it often takes time for enough data of the improved schema to be collected for us to adequately train a replacement model. The Bridged Schema pattern allows us to use as much of the newer data as is available, but augment it with some of the older data to improve model accuracy.

The Rebalancing pattern, Sara Robinson

Many real-world datasets are not perfectly balanced, and it’s important to address this throughout the ML process – in data analysis, model development, and in production. The Rebalancing pattern provides various approaches for handling datasets that are inherently imbalanced.

The Continued Model Evaluation pattern, Michale Munn
This pattern handles the common problem of detecting when a deployed model is no longer fit-for-purpose. This pattern addresses the problems of data and concept drift by regularly evaluating your model, and using these results to determine if retraining is necessary.

SPEAKERS

Valliappa Lakshmanan
@lak_gcp (https://twitter.com/lak_gcp)

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

Sara Robinson
@SRobTweets (https://twitter.com/SRobTweets)

Sara Robinson is a 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

Mike is an ML solutions engineer in Google Cloud. He helps customers design, implement, and deploy machine learning models and teaches the ML Immersion Program in Google's Advanced Solutions Lab.

When

When

Thursday, November 19, 2020
1:00 AM – 2:30 AM UTC

Organizers

  • Margaret Maynard-Reid

    GDG Organizer

  • Yenchi Lin

    GDG Organizer

  • clive boulton

    GDG

    Architect and Engineer

  • Vishal Pallerla

    GDG organizer

  • David Gamez

    GDG Organizer

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