Tag

ml

Julia Crash Course: Dictionaries

An excerpt from Julia as a Second Language by Erik Engheim

This article covers:

·   Storing values on keys in dictionaries.

·   Working with pair objects.

·   Using tuples to create dictionaries.

·   Comparing dictionaries and arrays.

Read it if you’re interested in the Julia language or in how it handles dictionaries.

Managing AI and ML Projects: a primer for success

An excerpt from Managing Machine Learning Projects by Simon Thompson

Managing Machine Learning Projects will teach you to guide machine learning projects from design to production—no machine learning experience required!

Read this article if you’re a project manager who works with machine learning applications.

Why Should You Program with Julia?

An excerpt from Julia as a Second Language by Erik Engheim

This article covers:

What type of problems Julia solves.
The limits of statically-typed languages.
Why the world needs a fast dynamically-typed language.
How Julia increases programmer productivity.

Read it if you’re interested in the Julia language and its strengths and weaknesses.

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.

Parameter Server Pattern: Tagging Entities in 8 Millions of YouTube Videos

From Distributed Machine Learning Patterns by Yuan Tang

In this article, we introduce the parameter server pattern which comes handy for situations where the model is too large to fit in a single machine such as one we would have to build for tagging entities in the 8 millions of YouTube videos.

Deploying Machine Learning Models, Part 5: deployment

In this series, we cover model deployment: the process of putting models to use. In particular, we’ll see how to package a model inside a web service, allowing other services to use it. We also show how to deploy the web service to a production-ready environment.

Deploying Machine Learning Models, Part 4: creating a Docker image

From Machine Learning Bookcamp by Alexey Grigorev

In this series, we cover model deployment: the process of putting models to use. In particular, we’ll see how to package a model inside a web service, allowing other services to use it. We also show how to deploy the web service to a production-ready environment.

Deploying Machine Learning Models, Part 3: managing dependencies

From Machine Learning Bookcamp by Alexey Grigorev In this series, we cover model deployment: the process of putting models to use. In particular, we’ll see how to package a model inside a web service, allowing other services to use it…. Continue Reading →

Deploying Machine Learning Models, Part 2: model serving

From Machine Learning Bookcamp by Alexey Grigorev

In this series, we cover model deployment: the process of putting models to use. In particular, we’ll see how to package a model inside a web service, allowing other services to use it. We also show how to deploy the web service to a production-ready environment.

Deploying Machine Learning Models, Part 1: saving models

From Machine Learning Bookcamp by Alexey Grigorev

In this series, we cover model deployment: the process of putting models to use. In particular, we’ll see how to package a model inside a web service, allowing other services to use it. We also show how to deploy the web service to a production-ready environment.

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