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

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Blog » General » This Week in Machine Learning: C.N.N in Archeology, Best Laptops, Voiceitt & Alexa, Major Scientific Breakthrough, and More!

This Week in Machine Learning: C.N.N in Archeology, Best Laptops, Voiceitt & Alexa, Major Scientific Breakthrough, and More!

The world is an everchanging place. It evolves every day and brings so many new, exciting discoveries that it can be difficult to stay on top. The same goes for machine learning and AI. It’s been two weeks since the latest weekly roundup and many new things have happened.

Check out the weekly roundup for the latest news from machine learning. News, analysis, interesting stories – only the best picks!. Read, learn, and get inspired!

Weekly Roundup: November 24 – December 7

» 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!

» How Archaeologists Are Using Deep Learning to Dig Deeper by Zach Zorich on The New York Times | November 24

Algorithms can be applied everywhere. Even in archeology.This is an interesting story (especially for the history enthusiasts) on how two researchers use a convolutional neural network to look for Scythian tombs. They hope that their creation will give archaeologists a way to find new tombs and to identify important sites so that they can be protected from looters.

» Voiceitt, Amazon Announce Collaboration Between Alexa And ‘Superpower’ Speech Technology by Steven Aquino on Forbes | December 3

Amazon and speech startup Voiceitt announced a collaboration that aims to make Alexa more accessible to people with atypical speech. Voiceitt is an app that uses machine-learning and speech recognition technologies to help those with speech impairments communicate and be more easily understood.

Voiceitt stated that it “recognized the opportunity to expand its technology offering to facilitate not only in-person communication but also interaction with voice activated and controlled devices.”

» Best Laptops for Machine Learning, Data Science, and Deep Learning by Towards AI Team on Towards AI blog | November 28

Every good data scientist, engineer, and even ML amateur needs a good laptop to work efficiently. 💻

The editorial team at Towards AI for the past year has looked at over 2,000 laptops and picked what they consider to be the best laptops for machine learning, data science, and deep learning for every budget. If you’re looking for a good equipment, this is the right place for you!

» Major Scientific Advance: DeepMind AI AlphaFold Solves 50-Year-Old Grand Challenge of Protein Structure Prediction by DeepMind on SciTechDaily | December 1

Extremely interesting, fascinating, and hope-giving article!

In a major scientific advance, the latest version of DeepMind’s AI system AlphaFold has been recognized as a solution to the 50-year-old grand challenge of protein structure prediction, often referred to as the ‘protein folding problem’, according to a rigorous independent assessment. 🧬

This breakthrough could significantly accelerate biological research over the long term, unlocking new possibilities in disease understanding and drug discovery among other fields!

» I asked GPT-3 for the question to “42”. I didn’t like its answer and neither will you by Bernhard Mueller | November 24

Can GPT-3 compute the ultimate question about life, the Universe and everything? If you’ve enjoyed the book The Hitchhiker’s Guide to the Galaxy (or the movie), you may find this read interesting. Smart, humorous, make sure to check it out if you have a bad day, or want to laugh a little, or want to know the answer to the question “42”.

» AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2020 and Key Trends for 2021 by Matthew Mayo on KDnuggets | December 4

Some insights into the 2020 and predictions on what the next year will bring.

» 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 week!


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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