Blog » General » This Week in Machine Learning: Amazon Releases Kendra, Brain-Inspired Algorithms, Confused AI Models & More

This Week in Machine Learning: Amazon Releases Kendra, Brain-Inspired Algorithms, Confused AI Models & More

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 the machine learning. Enjoy the read!

Weekly Roundup: May 11-18

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

Listen to our latest episode of Machine Learning That Works Podcast! An interview with a great artificial intelligence researcher and Chief AI Scientist in Lindera, Arash Azhand Tune in and enjoy! 🎧

> Top 20 Skills and Keywords for Data Science by Rashi Desai on Towards Data Science | May 12

Just as in the title 😉

> Amazon releases Kendra to solve enterprise search with AI and machine learning by Ron Miller on TechCrunch | May 11

Amazon is trying to change the enterprise search game by putting it into a more modern machine learning-driven context to use today’s technology to help you find that perfect response just as you typically do on the web.

> Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning on Google AI Blog | May 13

The authors propose a large-scale and diverse benchmark for measuring the competence of different image classification models in a realistic and challenging few-shot setting, offering a framework in which one can investigate several important aspects of few-shot classification.

Scientists Bridge Neuroscience With AI Machine Learning by Cami Rosso on Psychology Today | May 15

Researchers discover brain-inspired algorithms for faster AI learning.

After all, deep learning is somewhat inspired by the human intelligence of the biological brain. Make sure to check this short, interesting read!

> Our weird behavior during the pandemic is messing with AI models by Will Douglas Heavenarchive page on MIT Technology Review | May 11

Machine-learning models trained on normal behavior are showing cracks —forcing humans to step in to set them straight.

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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