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This Week in Machine Learning: Oceans, Marketers, and Wildlife Protection

Machine learning is fascinating. New things happen every second while we’re busy performing our daily tasks. If you want to know what big things have happened over the last week, make sure to check this weekly roundup!

Here are the best picks from the last week from the world of machine learning. Enjoy the read!

Weekly Roundup: May 26 – June 1

» blog – make sure to visit our blog to find out interesting and in-depth articles on machine learning from the last week.

» Machine learning helps map global ocean communities by Jennifer Chu on MIT News Office | May 29

A machine-learning technique developed at MIT combs through global ocean data to find commonalities between marine locations, based on interactions between phytoplankton species. Using this approach, researchers have determined that the ocean can be split into over 100 types of “provinces,” and 12 “megaprovinces,” that are distinct in their ecological makeup.

» Nearly half of marketers are already using or testing machine learning by Nikki Gilliland on Econsultancy | May 29

Interesting research on the adoption of ML in marketing.

» 10 Free Courses to learn Essential Python Machine Learning libraries on Becoming Human | May

In this article, the author shares some of the best free classes to learn Machine learning and Deep learning online.

» Machine learning helps protect Africa’s wildlife by Admire Moyo on ITweb | May 27

A partnership between German-based software giant SAP and non-profit organization Elephants, Rhinos and People (ERP) is using machine learning to protect endangered species in Africa. An honorable cause, make sure to check it out. 🦏

» KDnuggets™ News of the week with top stories and tweets of the past week, plus opinions, tutorials, events, webinars, meetings, and jobs.

» Old but gold, the reliable Reddit thread on ML for more news on machine learning. There’s always something for everyone – tips, tricks, hacks, and more news.

That’s all folks! I hope you found something of interest in this weekly roundup. Don’t forget to check our blog for more inspiring articles.

👉 Came across an interesting ML article? Or maybe you wrote one yourself and would like to share it with other people? Let us know, we’ll spread the news in our weekly roundup!


ML Experiment Tracking: What It Is, Why It Matters, and How to Implement It

10 mins read | Author Jakub Czakon | Updated July 14th, 2021

Let me share a story that I’ve heard too many times.

”… We were developing an ML model with my team, we ran a lot of experiments and got promising results…

…unfortunately, we couldn’t tell exactly what performed best because we forgot to save some model parameters and dataset versions…

…after a few weeks, we weren’t even sure what we have actually tried and we needed to re-run pretty much everything”

– unfortunate ML researcher.

And the truth is, when you develop ML models you will run a lot of experiments.

Those experiments may:

  • use different models and model hyperparameters
  • use different training or evaluation data, 
  • run different code (including this small change that you wanted to test quickly)
  • run the same code in a different environment (not knowing which PyTorch or Tensorflow version was installed)

And as a result, they can produce completely different evaluation metrics. 

Keeping track of all that information can very quickly become really hard. Especially if you want to organize and compare those experiments and feel confident that you know which setup produced the best result.  

This is where ML experiment tracking comes in. 

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