Machine Learning for Developers

By Satish Chandra Gupta

ML4Devs is a biweekly newsletter for software developers. The aim is to curate 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 biweekly newsletter for software developers.

The aim is to curate 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.

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#11・

Machine Learning vs. Traditional Software Development (ML4Devs Newsletter, Issue 11)

In the previous issue, we examined the MLOps ecosystem. It is a lot more complex compared to traditional software engineering projects. In this issue, let's understand the differences between:Traditional programs and Machine LearningSoftware Development Life …

 
#10・

MLOps for Continuous Integration, Delivery, and Training (ML4Devs Newsletter, Issue 10)

MLOps is a hot topic and everyone seems to be talking about it. I have been reading quite a bit of material, but I will share what I have learned so far.

 
#9・

When to (Not) Use Machine Learning (ML4Devs Newsletter, Issue 9)

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

 
#8・

Why Machine Learning Projects Fail (ML4Devs Newsletter, Issue 8)

Machine learning and its life cycle are 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.Before embarking on building ML applications, studyi…

 
#7・

MLOps -- the dust has not settled yet (ML4Devs Newsletter, Issue 7)

You must have noticed the buzz about MLOps.MLOps is the lifecycle, process, and tools for deploying machine learning models in production.There has been an explosion of MLOps vendors and tools. Many of those are named as xyzFlow or xyzML:KubeFlowMLFlowMetaFlo…

 
#6・

Data Visualization (ML4Devs Newsletter, Issue 6)

When you see informative charts and infographics, what is your first reaction? I wonder how you decide which chart type to use.In this issue, I will share some of the best resources I found.

 
#5・

Setting up Data Collection (ML4Devs Newsletter, Issue 5)

Cliché: Without data, there can be no data science.But it is true.While learning data science, we mostly use public data sets or scrape data off the web. But in ML-assisted products, most of the data is generated and collected through business applications.Th…

 
#4・

Best Path for Developers to Get into Machine Learning (ML4Devs Newsletter, Issue 4)

The most frequent question I get from developers is: what is the best way to get into Machine Learning?A few years back, my response was:Google for best resources and learnFind problems at work and apply what you learnRepeatThough that response was honest and…

 
#3・

To be agile, or not to be (ML4Devs Newsletter, Issue 3)

I guess the answer depends on whom do you ask.I have seen many Data Scientists bitterly oppose Agile and Scrum:6 Reasons why I think Agile Data Science does not workWhy Scrum is awful for data scienceWhy data science doesn't respond well to Agile methodologie…

 
#2・

ML Model Testing (ML4Devs Newsletter, Issue 2)

If your machine learning model has a high correctness score on the holdout test data set, is it safe to deploy it in production?

 
#1・

Machine Learning for Developers (ML4Devs Newsletter, Issue 1)

Wish you all a very happy new year! I hope that this year we finally put the COVID pandemic behind us.Last year, I started ML4Devs as a weekly newsletter but could not sustain the pace. So this year, I am restarting it as a biweekly to make it more sustainabl…