We Raised $8M Series A to Continue Building Experiment Tracking and Model Registry That “Just Works”

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This Week in Machine Learning: Depression Treatment, 2021 Trends, Supernova Explosions, and More

It’s almost Christmas time and the Holiday season is just around the corner! So before you indulge yourself in the great Holiday spirit, let’s see what’s been happening in the world of machine learning.

Here’s our bi-weekly summary of the best articles on the ML. Stories, news, discoveries. Let’s see what’s on the plate. Enjoy the read!

Weekly Roundup: December 8-21

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

We publish regularly, only the best content, written by experts. Make sure to check it out, there’s something for everyone!

» Artificial Intelligence Discovers Surprising Patterns in Earth’s Biological Mass Extinctions by Tokyo Institute of Technology on SciTech Daily | December 13

A new study applies machine learning to the fossil record to visualize life’s history, showing the impacts of major evolutionary events. This shows the long-term evolutionary and ecological impacts of major events of extinction and speciation.

This is an interesting read about the relevancy of creative destruction theory and the truth behind the extinction of the species. Check it out to learn how ML is applied is this kind of studies.

» Machine Learning Identifies New Brain Network Signature of Major Depression on Neuroscience News | December 7

According to the WHO, more than 264 million people of all ages suffer from depression. But there is hope for the better treatment of the illness. This article describes how using data from neuroimaging, a machine learning algorithm has identified key functional neural connections that could serve as a biomarker in the diagnosis of major depressive disorder.

Here’s the original research: Generalizable brain network markers of major depressive disorder across multiple imaging sites

» AI Experts Predict 2021 Trends on ReWork Blog | December 10

As 2020 comes to an end, the whole world wonders what will 2021 bring. What do the AI experts think? Interesting insights with the sources for further reading. A must-read for any data scientist and ML enthusiast!

» Main 2020 Developments and Key 2021 Trends in AI, Data Science, Machine Learning Technology by Gregory Piatetsky on KDnuggets

A great summary of the past and upcoming year, trends, and opinions from Marcus Borba, Kirk Borne, Tom Davenport, Carla Gentry, Jake Flomenberg, IPFConline (Pierre Pinna), Nikita Johnson, Doug Laney, Ronald van Loon, Bill Schmarzo, Kate Strachnyi, and Mark van Rijmenam. Straight from KDnuggets!

» Industry 2021 Predictions for AI, Analytics, Data Science, Machine Learning by Gregory Piatetsky on KDnuggets

Another insight from KDnuggets, this time about the trends for 2021. Industry predictions from 12 innovative companies – what key trends they expect in 2021 in AI, Analytics, Data Science, and Machine Learning?

» How Michelle K. Lee plans to help businesses tap into the potential of machine learning on Amazon Science | December 13

Here’s a concise and interesting interview with Michelle K. Lee, vice president of the Amazon Machine Learning Solutions Lab at Amazon Web Services in which she shares the lessons she learned leading a 200-year-old government agency — and why she’s excited about the future.

» Artificial intelligence classifies supernova explosions with unprecedented accuracy by Center for Astrophysics | Harvard & Smithsonian on phys.org | December 17

Artificial intelligence is classifying real supernova explosions without the traditional use of spectra, thanks to a team of astronomers at the Center for Astrophysics | Harvard & Smithsonian. The complete data sets and resulting classifications are publicly available for open use.

Although this is not the first machine learning project for supernovae classification, it is the first time that astronomers have had access to a real data set large enough to train an artificial intelligence-based supernovae classifier, making it possible to create machine learning algorithms without the use of simulations.

» 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! Have you found something of interest in this weekly roundup? We hope you got inspired! Don’t forget to check our blog for more inspiring articles.

And if you came across an interesting ML article, or maybe wrote one yourself and would like to share it with other people, let us know, we’ll spread the news in our weekly roundup! See you next time and enjoy your Holidays! 🤶


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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