Practical AI

As you start developing an AI/ML based solution, you quickly figure out that you need to run workflows. Not only that, you might need to run those workflows across various kinds of infrastructure (including GPUs) at scale. Ville Tuulos developed Metaflow while working at Netflix ...

Any AI play that lacks an underlying data strategy is doomed to fail, and a big part of any data strategy is labeling. Michael, from Label Studio, joins us in this episode to discuss how the industry’s perception of data labeling is shifting. We cover open source tooling, validat...

Yonatan Geifman of Deci makes Daniel and Chris buckle up, and takes them on a tour of the ideas behind his amazing new inference platform. It enables AI developers to build, optimize, and deploy blazing-fast deep learning models on any hardware. Don’t blink or you’ll miss it!

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In this episode, Peter Wang from Anaconda joins us again to go over their latest “State of Data Science” survey. The updated results include some insights related to data science work during COVID along with other topics including AutoML and model bias. Peter also tells us a bit ...

We’re back with another Fully Connected episode – Daniel and Chris dive into a series of articles called ‘A New AI Lexicon’ that collectively explore alternate narratives, positionalities, and understandings to the better known and widely circulated ways of talking about AI. The ...

In Kenya, 33% of maternal deaths are caused by delays in seeking care, and 55% of maternal deaths are caused by delays in action or inadequate care by providers. Jacaranda Health is employing NLP and dialogue system techniques to help mothers experience childbirth safely and with...

SLICED is like the TV Show Chopped but for data science. Competitors get a never-before-seen dataset and two-hours to code a solution to a prediction challenge. Meg and Nick, the SLICED show hosts, join us in this episode to discuss how the show is creating much needed data scien...

AI is being used to transform the most personal instrument we have, our voice, into something that can be “played.” This is fascinating in and of itself, but Yotam Mann from Never Before Heard Sounds is doing so much more! In this episode, he describes how he is using neural nets...

Inspired by a recent article from Erik Bernhardsson titled “Building a data team at a mid-stage startup: a short story”, Chris and Daniel discuss all things AI/data team building. They share some stories from their experiences kick starting AI efforts at various organizations and...

9 out of 10 AI projects don’t end up creating value in production. Why? At least partly because these projects utilize unstable models and drifting data. In this episode, Roey from BeyondMinds gives us some insights on how to filter garbage input, detect risky output, and general...

How did we get from symbolic AI to deep learning models that help you write code (i.e., GitHub and OpenAI’s new Copilot)? That’s what Chris and Daniel discuss in this episode about the history and future of deep learning (with some help from an article recently published in ACM a...

Pinecone is the first vector database for machine learning. Edo Liberty explains to Chris how vector similarity search works, and its advantages over traditional database approaches for machine learning. It enables one to search through billions of vector embeddings for similar m...