Snorkel AI

Braden Hancock joins Chris to discuss Snorkel Flow and the Snorkel open source project. With Flow, users programmatically label, build, and augment training data to drive a radically faster, more flexible, and higher quality end-to-end AI development and deployment process. Sponsors: DigitalOcean – Get apps to market faster. Build, deploy, and scale apps quickly using a simple, fully managed solution. DigitalOcean handles the infrastructure, app runtimes and dependencies, so that you can push code to production in just a few clicks. Try it free with $100 credit at do.co/changelog.  Changelog++ – You love our content and you want to take it to the next level by showing your support. We’ll take you closer to the metal with no ads, extended episodes, outtakes, bonus content, a deep discount in our merch store (soon), and more to come. Let’s do this!  Fastly – Our bandwidth partner. Fastly powers fast, secure, and scalable digital experiences. Move beyond your content delivery network to their powerful edge cloud platform. Learn more at fastly.com.  LaunchDarkly – Power experimentation at any scale. Fast and reliable feature management for the modern enterprise.  Featuring: Braden Hancock – Website, X Chris Benson – Website, GitHub, LinkedIn, X Show Notes: Snorkel AI Snorkel OSS Snorkel Blog Snorkel AI | Twitter Snorkel AI | LinkedIn Snorkel Best of VLDB paper Snorkel Drybell collaboration with Google Jerod recommends Getting Waymo into autonomous driving (Drago Anguelov) Building the world’s most popular data science platform (Peter Wang) Achieving provably beneficial, human-compatible AI (Stuart Russell) Upcoming Events:  Register for upcoming webinars here!

Braden Hancock

Co-founder

Snorkel AI

Alex Ratner, an assistant professor at the University of Washington and a cofounder and CEO of Snorkel AI, talks about programmatically labeling training data for supervised learning models.

Alex Ratner

PhD Student

Stanford UniversitySnorkel AI

Today we’re joined by Alex Ratner, Ph.D. student at Stanford, to discuss: • Snorkel, the open source framework that is the successor to Stanford's Deep Dive project. • How Snorkel is used as a framework for creating training data with weak supervised learning techniques. • Multiple use cases for Snorkel, including how it is used by companies like Google.  The complete show notes can be found at twimlai.com/talk/270. Follow along with AI Platforms Vol. 2 at twimlai.com/aiplatforms2.

Alex Ratner

PhD Student

Stanford UniversitySnorkel AI

Summary The majority of the conversation around machine learning and big data pertains to well-structured and cleaned data sets. Unfortunately, that is just a small percentage of the information that is available, so the rest of the sources of knowledge in a company are housed in so-called “Dark Data” sets. In this episode Alex Ratner explains how the work that he and his fellow researchers are doing on Snorkel can be used to extract value by leveraging labeling functions written by domain experts to generate training sets for machine learning models. He also explains how this approach can be used to democratize machine learning by making it feasible for organizations with smaller data sets than those required by most tooling. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I’m interviewing Alex Ratner about Snorkel and Dark Data Interview Introduction How did you get involved in the area of data management? Can you start by sharing your definition of dark data and how Snorkel helps to extract value from it? What are some of the most challenging aspects of building labelling functions and what tools or techniques are available to verify their validity and effectiveness in producing accurate outcomes? Can you provide some examples of how Snorkel can be used to build useful models in production contexts for companies or problem domains where data collection is difficult to do at large scale? For someone who wants to use Snorkel, what are the steps involved in processing the source data and what tooling or systems are necessary to analyse the outputs for generating usable insights? How is Snorkel architected and how has the design evolved over its lifetime? What are some situations where Snorkel would be poorly suited for use? What are some of the most interesting applications of Snorkel that you are aware of? What are some of the other projects that you and your group are working on that interact with Snorkel? What are some of the features or improvements that you have planned for future releases of Snorkel? Contact Info Website ajratner on Github @ajratner on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Stanford DAWN HazyResearch Snorkel Christopher Ré Dark Data DARPA Memex Training Data FDA ImageNet National Library of Medicine Empirical Studies of Conflict Data Augmentation PyTorch Tensorflow Generative Model Discriminative Model Weak Supervision The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Alex Ratner

PhD Student

Stanford UniversitySnorkel AI