MLOps (also known as DevOps for machine learning) is the practice of collaboration and communication between data scientists and DevOps professionals to help manage the production machine learning (ML) lifecycle.

Azure Machine Learning service’s MLOps capabilities provide customers with asset management and orchestration services which enable effective ML lifecycle management.

Learn more about MLOps:

Siraj Raval just posted this video on defending AI against adversarial attacks

Machine Learning technology isn’t perfect, it’s vulnerable to many different types of attacks! In this episode, I’ll explain 2 common types of attacks and 2 common types of defenses using various code demos from across the Web. There’s some really dope mathematics involved with adversarial attacks, and it was a lot of fun reading about the ‘cat and mouse’ game between new attack techniques, followed by new defense techniques. I encourage anyone new to the field who finds this stuff interesting to learn more about it. I definitely plan to. Let’s look into some math, code, and examples. Enjoy!

Slideshow for this video:

Demo project:


Microsoft Research explores how the brains beget the mind.

How do molecules, cells, and synapses effect reasoning, intelligence, language, science? Despite dazzling progress in experimental neuroscience we do not seem to be making progress in the overarching question — the gap is huge and a completely new approach seems to be required.

As Richard Axel recently put it: “We don’t have a logic for the transformation of neural activity into thought.” What kind of formal system would qualify as this “logic”? I will sketch a possible answer.

(Joint work with Santosh Vempala, Dan Mitropolsky, Mike Collins, Wolfgang Maass, and Larry Abbott.)

Talk slides: