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: DRL, Algorithms Evolution, Interview with ML Engineer from Amazon & More

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 goes a dose of the latest news, discoveries, and inspiring stories from the world of Machine Learning. There is something for everyone. Enjoy your read!

Weekly Roundup: April 15 – 26

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

Also, we’ve recently launched a podcast so tune in and enjoy! 🎧

Chip Design with Deep Reinforcement Learning on Google AI Blog | April 23

The authors pose chip placement as a reinforcement learning (RL) problem, where they train an agent (i.e, an RL policy) to optimize the quality of chip placements.

> Springer has released 65 Machine Learning and Data books for free by Uri Eliabayev on Towards Data Science blog | April 26

Springer has released hundreds of free books on a wide range of topics to the general public. The list, which includes 408 books in total, covers a wide range of scientific and technological topics. Here’s the list of all the books (65 in number) that are relevant to the data and Machine Learning field.

The Amazon Machine Learning Engineer Interview by Terence Shin on Towards Data Science Blog | April 21

Understanding Amazon’s culture, hiring process, and interview questions.

Make sure to read the interview if you see yourself working in the future as an ML engineer at Amazon.

> This Is How Algorithms Will Evolve Themselves by Courtney Linder on Pop Mech | April 23

Google is borrowing from Darwin to make a seismic leap in automatic machine learning. It could spell out the end of most human bias.

> From mythology to machine learning, a history of artificial intelligence by Katia Patin on Coda Story | April 23

While AI now powers smart cities, driverless cars and home appliances, ethical concerns about the technology have existed for centuries.

In this interesting read, the author presents  nine important milestones in the history of AI and the ethical concerns that have long loomed over the field.

> 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

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. 

Continue reading ->
19 Best JupyterLab Extensions for Machine Learning

19 Best JupyterLab Extensions for Machine Learning

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Data Augmentation in Python: Everything You Need to Know

Data Augmentation in Python: Everything You Need to Know

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How to Make Sense of the Reinforcement Learning Agents? What and Why I Log During Training and Debug

How to Make Sense of the Reinforcement Learning Agents? What and Why I Log During Training and Debug

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