Machine Learning for Developers

By Satish Chandra Gupta

ML4Devs is a weekly newsletter for software developers. The aim is to curate and create resources for practitioners to design, develop, deploy, and maintain ML applications at scale to drive measurable positive business impact. Each issue discusses a topic from a developer’s viewpoint.

ML4Devs is a weekly newsletter for software developers.

The aim is to curate and create resources for practitioners to design, develop, deploy, and maintain ML applications at scale to drive measurable positive business impact.

Each issue discusses a topic from a developer’s viewpoint.

By subscribing, you agree with Revue’s Terms of Service and Privacy Policy and understand that Machine Learning for Developers will receive your email address.

1.2K

subscribers

8

issues

#8・

Setting up Data Collection (ML4Devs, Issue 8)

Cliché: Without data, there can be no data science.

 
#7・

Machine Learning Life Cycle (ML4Devs, Issue 7)

A reader pointed out that I paid only passing attention to the ML life cycle in Machine Learning vs. Traditional Software issue.

 
#6・

MLOps for Continuous Integration, Delivery, and Training of ML Models (ML4Devs, Issue 6)

Continuing on the theme of “Integrate Early and Iterate Often” from the previous issue, the obvious question is how to do it well. We touched upon ML Pipeline briefly. In this issue, let’s examine how the best in the business do it.

 
#5・

Setting Up a Machine Learning Project (ML4Devs, Issue 5)

In the Why Machine Learning Projects Fail issue, we saw that the learnings from software development can be applied in ML too to improve chances of success:

 
#4・

When to (Not) Use Machine Learning (ML4Devs, Issue 4)

In the previous issue, I discussed why Machine Learning projects fail. In this issue, let’s start figuring how to build successful Machine Learning products. The first step is to understand when Machine Learning is more effective than traditional programming.

 
#3・

Why Machine Learning Projects Fail (ML4Devs, Issue 3)

In the previous issue, I discussed how machine learning and its life cycle is different from traditional programming and software development life cycle. Since ML is new and different, we do not yet understand it as well as traditional software.

 
#2・

Machine Learning vs. Traditional Software Development (ML4Devs, Issue 2)

In the previous issue, I discussed the experiences of data scientists and developers building real-world Machine Learning applications:

 
#1・

Introducing Machine Learning for Developers Newsletter (ML4Devs, Issue 1)

Hi there,