Subject

Articles

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.

Adding LaTeX Rendering to Our Website, Part 1

From Hugo in Action by Atishay Jain

This article is all about adding LaTeX rendering to a static website built with Hugo.

Asking in Akka

From Akka in Action, Second Edition by Francisco Lopez-Sancho Abraham

This article jumps into the basics of how “asking” in Akka works.

Collective Communication Pattern: Improving Performance When Parameter Servers Become a Bottleneck

From Distributed Machine Learning Patterns by Yuan Tang

In this article, we introduce the collective communication pattern, which is a great alternative to parameter servers when the machine learning model we are building is not too large without having to tune the ratio between the number of workers and parameter servers.

Clustering Data into Groups, Part 3

From Data Science Bookcamp by Leonard Apeltsin

This 3-part article series covers:

Clustering data by centrality
Clustering data by density
Trade-offs between clustering algorithms
Executing clustering using the scikit-learn library
Iterating over clusters using Pandas

Clustering Data into Groups, Part 2

From Data Science Bookcamp by Leonard Apeltsin

This 3-part article series covers:

Clustering data by centrality
Clustering data by density
Trade-offs between clustering algorithms
Executing clustering using the scikit-learn library
Iterating over clusters using Pandas

Clustering Data into Groups, Part 1

From Data Science Bookcamp by Leonard Apeltsin

This 3-part article series covers:

Clustering data by centrality
Clustering data by density
Trade-offs between clustering algorithms
Executing clustering using the scikit-learn library
Iterating over clusters using Pandas

Building a Memory Game using Unity’s 2D Functionality

From Unity in Action, Third Edition by Joe Hocking

A First asyncio Application

From Python Concurrency with asyncio by Matthew Fowler

This article shows how you might make your first application that leverages asyncio.

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