Summary
Data engineers have typically left the process of data labeling to data scientists or other roles because of its nature as a manual and process heavy undertaking, focusing instead on building automation and repeatable systems. Watchful is a platform to make labeling a repeatable and scalable process that relies on codifying domain expertise. In this episode founder Shayan Mohanty explains how he and his team are bringing software best practices and automation to the world of machine learning data preparation and how it allows data engineers to be involved in the process.
Announcements
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IntroductionHow did you get involved in the area of data management?Can you describe what Watchful is and the story behind it?What are your core goals at Watchful?What problem are you solving and who are the people most impacted by that problem?What is the role of the data engineer in the process of getting data labeled for machine learning projects?Data labeling is a large and competitive market. How do you characterize the different approaches offered by the various platforms and services?What are the main points of friction involved in getting data labeled?How do the types of data and its applications factor into how those challenges manifest?What does Watchful provide that allows it to address those obstacles?Can you describe how Watchful is implemented?What are some of the initial ideas/assumptions that you have had to re-evaluate?What are some of the ways that you have had to adjust the design of your user experience flows since you first started?What is the workflow for teams who are adopting Watchful?What are the types of collaboration that need to happen in the data labeling process?What are some of the elements of shared vocabulary that different stakeholders in the process need to establish to be successful?What are the most interesting, innovative, or unexpected ways that you have seen Watchful used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Watchful?When is Watchful the wrong choice?What do you have planned for the future of Watchful?Contact Info
LinkedIn@shayanjm on TwitterParting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements
Thank you for listening! Don’t forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workersLinks
WatchfulEntity ResolutionSupervised Machine LearningBERTCLIPLabelBoxLabel StudioSnorkel AIMachine Learning Podcast EpisodeRegEx == Regular ExpressionREPL == Read Evaluate Print LoopIDE == Integrated Development EnvironmentTuring CompletenessClojureRustNamed Entity RecognitionThe Halting ProblemNP HardLidarShayan: Arguments Against Hand LabelingThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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