The Determined Podcast Series examines various aspects of the machine learning pipeline - including data preparation, model development, hardware management, and deployment - with some of the brightest minds in AI.
Tianqi Chen on Deployable Machine Learning Algorithms
Over the last couple of months, we’ve had fascinating conversations with some of the most brilliant minds in AI. We’ve covered the golden age of specialized hardware, the importance of data wrangling to achieve reproducibility, and why programmatic data labeling is a vital step in machine learning. To wrap up the Determined Podcast Series, we sat down with OctoML CTO and co-founder Tian “TQ” Chen. TQ holds a PhD in machine learning and systems from the University of Washington, and recently accepted an assistant professor role at Carnegie Mellon University. TQ reviews the philosophy behind TVM, the problem set that OctoML is solving, and how to make ML models deployable on specialized hardware.
Neil Conway On the Challenges of Deep Learning Model Development
So far In the Determined Podcast Series we've spoken with experts in hardware and different parts of the machine learning workflow. Today's episode features two of Determined founders - Ameet Talwalkar and Neil Conway - and peels back the layers on Determined's role in a burgeoning deep learning ecosystem.
Alex Ratner on Programmatic Data Labeling for Machine Learning
In our last episode, we talked about the importance of data preparation for reproducibility in machine learning. Our conversation this week follows a similar path with Alex Ratner, co-founder and CEO of the data labeling company Snorkel AI and Assistant Professor at the University of Washington. We talked with Alex about Snorkel’s programmatic data labeling approach, the evolution of bottlenecks in machine learning, and our common goal of helping folks develop AI applications faster.
Joe Hellerstein on Data Wrangling and Reproducibility
It is well known that machine learning is powered by data. Unfortunately, the raw data that we would like to use to train models is often created and stored in such a way that it is not machine consumable. As part of our Determined Podcast Series, Craig and I recently had a conversation with Joe Hellerstein, a computer science professor at UC Berkeley, a leading researcher in the databases community, and co-founder of the data wrangling company Trifacta. Joe talked about the unique challenges of data preparation, and how it blends ideas from software engineering and media editing.
A Conversation With Dave Patterson
I recently began a collaboration with Craig Smith, a longtime writer for The New York Times and host of the Eye on A.I. podcast, to chat with some of my friends and colleagues about various aspects of the machine learning pipeline, including data preparation, model development, hardware management, and deployment. Today we’re excited to release our first podcast, with my friend and mentor Dave Patterson.
Dave is one of the world’s foremost experts in semiconductor architecture, is helping to lead Google’s TPU project, and is a recipient of the 2017 Turing Award. Our conversation touched on a wide range in topics, including the end of Moore’s law, the recent Cambrian explosion of specialized AI chips, and the increased importance of RISC-V given the impending sale of ARM to NVIDIA. You can check out the podcast below, as well as the full interview transcript and a few highlights from our conversation.