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Training and Deployment Pipeline, Part 2

From Deep Learning Patterns and Practices by Andrew Ferlitsch

This article covers:

Feeding models training data in a production environment.
Scheduling for continuous retraining.
Using version control and evaluating models before and after deployment.
Deploying models for large scale on-demand and batch requests, in both monolithic and distributed deployments.

Training and Deployment Pipeline

From Deep Learning Patterns and Practices by Andrew Ferlitsch

This article covers:

●      Feeding models training data in a production environment.

●      Scheduling for continuous retraining.

●      Using version control and evaluating models before and after deployment.

●      Deploying models for large scale on-demand and batch requests, in both monolithic and distributed deployments.

Machine Learning as a Pipeline

From Deep Learning Patterns and Practices by Andrew Ferlitsch

Like the best software engineering, modern deep learning uses a pipeline architecture based on reusable patterns.

When Machine Learning Becomes Machine Design: new paradigms and patterns for automated deep learning

Andrew Ferlitsch reveals new paradigms–and patterns–for automated deep learning

Andrew Ferlitsch, from the developer relations team at Google Cloud AI, is so far out on the cutting edge of machine learning and artificial intelligence that he has to invent new terminology to describe what’s happening in Cloud AI with Google Cloud’s enterprise clients. In this interview with editors at Manning Publications, he talks about the current and coming changes in machine learning systems, starting with the concept of model amalgamation. Ferlitsch is currently writing a book, Deep Learning Design Patterns, which collects his ideas along with the most important composable model components.

Read more author interviews here

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