source: corporatefinanceinstitute.com

Building a machine learning model is interesting, but narrowing it down to a data product that end users can benefit from is more interesting to me. …


This is the concluding part of a three-part series. I suggest you read part 1 & 2 for a better understanding:

In this part, we will continue on building ML-powered apps using TDD approach with a special focus on the following:

  • Code Coverage and CI with Travis
  • Test Automation with…


This is the second part of a three-part series. I suggest you read part 1 for a better understanding:

In this article, we will continue on building ML-powered apps using TDD approach with special focus on the following:

  • Test organisation in sources code
  • Applicable tests with examples (hands-on)
  • Mastering test…


An approach to Test-Driven Development for ML-Powered Apps

Test-Driven Development (TDD) is a development technique where you must first write a test that fails before you write new functional code. …


Deep Dive Into Containerization for Data Science & Machine Learning

image source: neptune.ai

This is a summary of a well-detailed article I originally published with neptune.ai. For complete understanding and further details, you can find the full write-up by clicking the Full article link on neptune.ai

When building data science and machine learning-powered…


ML stages in an independent system by Google ML unit

This is the concluding part of a four-part series. I suggest you read part 1, 2, and 3 for a better understanding:

Part 1

Part 2

Part 3

In this article, we will continue building production machine learning systems on GCP with a special focus on the design of hybrid…


Photo by C Dustin on Unsplash

This is the third part of a four-part series. I suggest you read part 1 and 2 for a better understanding:

Part 1

Part 2

In this article, we will continue the exploration of production machine learning systems on GCP with a special focus on the design of high-performance ML…


Photo by C Dustin on Unsplash

This is the second part of a four-part series. I suggest you read part 1 for a better understanding:

link to part 1

In this article, we will continue on building production machine learning systems on GCP with a special focus on the following:

  • Data ingestion for cloud-based analytics and…


Photo by C Dustin on Unsplash

At the end of this series, you should be comfortable with ML systems designs (appropriate training and serving paradigm, and serve ML models scalably)

Content

  • Architecting production ML systems (part 1)
  • Static and dynamic training (part 1)
  • Static and dynamic inference (part 1)
  • Data ingestion for cloud-based analytics and ML (part…


This is the third and final part of a three-part series. I suggest you read parts 1 and 2 first for better understanding.

In the first part of the series, we got introduced to danfo.js, a new JavaScript package that provides fast, flexible, and expressive data structures designed to make…

Bamigbade Opeyemi

Data Scientist |ML-Engineer at Data Science Nigeria. Open to consulting and new opportunities. https://opeyemibami.github.io/yhemmy/

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