Tag

patterns

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.

Good and bad flexibility in code

By Christian Clausen

Flexibility is essential for long-lived codebases, however, it comes at a price. It requires effort to maintain flexibility and improperly-implemented flexibility can actually make a codebase harder to maintain. In this article, I show how good flexibility can be extracted from the structure of our code with minimal effort. But before we get into that, let’s discuss what flexibility in a codebase means.

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

Call or Pass?

From Five Lines of Code by Christian Clausen

This article delves into when a function should call a method on an object or pass it on as an argument.

Refactor code like a pro

From Five Lines of Code by Christian Clausen

The World of SOA Patterns

You might not even agree with an SOA-based approach, but are perhaps forced into using it based on someone else’s decision. Alternatively, you may think that SOA is the greatest thing since sliced bread. This article from SOA Patterns explains patterns that will help you make the right decisions for the particular challenges and requirements you’ll face in your SOA projects.

The World of SOA Patterns (PDF)

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