Abnormal Engineering Stories, Episode #2: Building ML to Make Humans More Effective

July 7, 2021
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Over the last three years building our ML-based cybersecurity products at Abnormal Security, I’ve benefitted enormously from discussions with colleagues in the ML space. This podcast aims to make some of those conversations available.

In our second episode of Abnormal Engineering Stories, Nico Koumchatzky and I discuss the future of ML platform, what it means to be an ML engineer, and the machine learning challenges faced at Abnormal and Nvidia. Nico is the Senior Director of AI Infrastructure at Nvidia, and before that, he ran Twitter’s ML Platform team, “Twitter Cortex,” where he and I worked together.

This discussion includes:

  • A wide-ranging and enjoyable discussion on the current and future state of ML platform with analogies to the history of software engineering
  • The role of the “ML Engineer” and why any successful ML practitioner needs to have one foot in the software engineering world of code, IDEs, databases, services, etc. and the other foot in the machine learning world with experimentation, data science, algorithms, etc.
  • Challenges we are trying to solve in our organizations including stopping cybercrime at Abnormal and building a platform to enable fast and large-scale ML and autonomous vehicles at Nvidia!

We hope you enjoy it! Please do subscribe on Apple, Spotify, or Google Podcasts.


Previous
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Business email compromise (BEC) is the most significant cybersecurity threat to enterprise organizations, with $1.8 billion in reported losses in 2020 alone. This type of email attack occurs when a cybercriminal uses social engineering to impersonate a trusted contact...
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It’s one thing to add machine learning and artificial intelligence features to an existing software platform. It’s quite another to build an entire company like Abnormal Security around machine learning technology, and to provide practical, everyday value to enterprise organizations.
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