Episode 123: AI Monitoring, Product-Oriented Data Science, and the Israeli ML community with Itai Bar Sinai

Show Notes(01:46) Itai reflected on his education at The Hebrew University of Jerusalem, studying Math and Computer Science.(04:18) Itai walked through his time as a software engineer at Google working in Google Trends.(06:56) Itai emphasized the importance of a software checklist within Google's engineering culture.(08:55) Itai explained how he became fascinated with AI/ML engineering.(10:31) Itai touched on his period working as an AI consultant.(13:28) Itai talked about his side hustle as a co-owner of Lia's Kitchen, a 100% vegan restaurant in Berlin.(16:13) Itai shared the founding story of Mona Labs, whose mission is to make AI and machine learning impactful, effective, reliable, and safe for fast-growth teams and businesses.(21:25) Itai unpacked the architecture overview of the Mona monitoring platform.(24:50) Itai talked about the early days of Mona finding design partners.(27:15) Itai dissected his perspective on a comprehensive monitoring strategy.(31:42) Itai explained why the secret to comprehensive monitoring lies in granular tracking and avoiding noise.(38:35) Itai explained how Mona can support real-time monitoring across the layers of the platform.(43:18) Itai mentioned the integration with New Relic to display the variability of use cases for Mona.(46:08) Itai discussed the shift for data science teams from being research-oriented to product-oriented.(51:03) Itai provided four tactics for data science teams to become "product-oriented."(58:04) Itai shared valuable hiring lessons to attract the right people who are excited about the mission of Mona Labs.(01:01:46) Itai provided his mental model for finding exceptional engineering talent.(01:03:38) Itai brought up again the importance of finding lighthouse customers.(01:05:46) Itai gave his thoughts on building the product to satisfy different customer needs.(01:07:55) Itai described the thriving ML engineering community in Israel.(01:09:42) Closing thoughtsItai's Contact InfoLinkedInMona Labs' ResourcesWebsite | LinkedIn | Twitter | YouTubeAbout | Customers | CareersPlatformBlog | Case Studies | DocsMentioned ContentBlog Posts and TalksWe are building Mona to bring ML observability to production AIThe definitive guide to AI/ML monitoringThe secret to successful AI monitoring: Get granular, but avoid noiseTaking AI from good to great by understanding it in the real world (June 2022)Data drift, concept drift, and how to monitor for themThe issues ML model retraining won't solveCommon pitfalls to avoid when evaluating an ML monitoring solutionIntroducing automated exploratory data analysis powered by MonaBest practices for setting up monitoring operations for your AI teamThe challenges of specificity in monitoring AIIs your LLM application ready for the public?Overcoming cultural shifts from data science to prompt engineeringPeopleGoku Mohandas (Creator of Made With ML)Ville Tuulos (CEO and Co-Founder of Outerbounds)Nimrod Tamir (CTO and Co-Founder of Mona Labs)Notes

My conversation with Itai was recorded back in October 2022. Since then, Mona Labs has introduced a new self-service monitoring solution for GPT! Read Itai's blog post for the technical details.

Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.

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