Compression is understanding
The home team chats about machine learning and its applications beyond the hot topic of GenAI, what it means for models to unlearn data, the future of open source, and new frontiers in game development.
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machine learning
MosaicML: Deep learning models for sale, all shapes and sizes (Ep. 577)
Ben and Ryan talk with Jonathan Frankle and Abhinav Venigalla of MosaicML, a startup trying to make deep learning and generative AI efficient and accessible for everyone.
ML and AI consulting-as-a-service (Ep. 542)
The home team talks with Jaclyn Rice Nelson, cofounder and CEO of Tribe AI, about the explosion of hype surrounding generative AI, what it’s like to work at a startup after working at Google, and how Tribe is leveraging the power of a specialist network.
The future of software engineering is powered by AIOps and open source (Ep. 523)
Hear how Intuit is using AI to help its dev teams ship faster.
Getting your data in shape for machine learning
Machine learning uses data structures that don't always resemble the ones used in standard computing. You'll need to process your data first if you want efficient machine learning.
Privacy-friendly machine learning data sets: synthetic data
Statistically-relevant data, but not actually exploitable.
How machine learning algorithms figure out what you should watch next
Curation at scale needs to process a lot of data with a good algorithm.
The luckiest guy in AI (Ep. 477)
Serial entrepreneur Varun Ganapathi joins the home team for a conversation about the intersection of physics, machine learning, and AI. He offers some recommendations for developers looking to get started in the ML/AI space and shares his own path from academia to entrepreneurship.
Using synthetic data to power machine learning while protecting user privacy
On this episode, we talk to John Myers, CTO and cofounder of Gretel, a company that provides synthetic data for training machine learning models without exposing any of their customers personally identifiable information.
Building a QA process for your deep learning pipeline in practice
Deep learning models still need testing, but many of the common testing approaches don't apply. But with the right methods, you can still make sure your pipeline produces good results.
Let’s enhance: use Intel AI to increase image resolution in this demo
Across alien epics and procedural crime dramas, detectives and truth seekers have repeated the mantra: zoom and enhance. It’s passed into popular culture as a much-beloved meme, but in recent years, machine learning has increasingly made this fiction trope into an accessible reality. And we've got the demo to prove it.
How to put machine learning models into production
The goal of building a machine learning model is to solve a problem, and a machine learning model can only do so when it is in production and actively in use by consumers. As such, model deployment is as important as model building.
