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This Week in Machine Learning: Bees, Sky Objects, & HMI

Every day brings new opportunities. A week brings even more. To make sure you’re getting the most out of it, we’ve gathered a list of trending articles of the week – everything about Data Science, AI, tech, and machine learning.

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

Weekly Roundup: May 19-25

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

» Microsoft launches new tools for building fairer machine learning models by Frederic Lardinois on Tech Crunch | May 19

Microsoft announced WhiteNoise, a new open-source toolkit that’s available both on GitHub and through Azure Machine Learning. WhiteNoise is the result of a partnership between Microsoft and Harvard’s Institute for Quantitative Social Science.

⇒ Read the original story on Microsoft blog: Microsoft responsible machine learning capabilities build trust in AI systems, developers say

» Neural Networks And Machine Learning Are Powering A New Era Of Perceptive Intelligence by Saleel Awsare on Forbes | May 19

A short, concise read on how human-machine interface (HMI) is an important component in improving the user experience when it comes to connected devices

» SAS boosts healthy bee populations through advanced analytics and machine learning on SAS | May 20

We all know how important bees are. What do they have in common with machine learning? In honor of World Bee Day, SAS announced three innovative projects to monitor, protect, and save the bees.

» Classification of Sky Objects with Machine Learning by Alper Çakır on Towards Data Science | May 19

If you enjoy astronomy, make sure to read the article where the author describes how he worked on his project of classifying sky objects with ML. 🌌

» 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!


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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. 

Continue reading ->
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