MLOps Coffee Sessions #166 with Roy Hasson & Santona Tuli, Eliminating Garbage In/Garbage Out for Analytics and ML. // Abstract Shift left data quality ownership and observability that makes it easy for users to catch bad data at the source and stop it from entering your analytics/ML stack. // Bio Santona Tuli Santona Tuli, Ph.D. began her data journey through fundamental physics—searching through massive event data from particle collisions at CERN to detect rare particles. She’s since extended her machine learning engineering to natural language processing, before switching focus to product and data engineering for data workflow authoring frameworks. As a Python engineer, she started with the programmatic data orchestration tool, Airflow, helping improve its developer experience for data science and machine learning pipelines. Currently, at Upsolver, she leads data engineering and science, driving developer research and engagement for the declarative workflow authoring framework in SQL. Dr. Tuli is passionate about building, as well as empowering others to build, end-to-end data and ML pipelines, scalably. Roy Hasson Roy is the head of product at Upsolver helping companies deliver high-quality data to their analytics and ML tools. Previously, Roy led product management for AWS Glue and AWS Lake Formation. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links https://royondata.substack.com/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Roy on LinkedIn: https://www.linkedin.com/in/royhasson/ Connect with Santona on LinkedIn: https://www.linkedin.com/in/santona-tuli/ Timestamps: [00:00] Santona's and Roy's preferred coffee [01:05] Santona's and Roy's background [03:33] Takeaways [05:49] Please like, share, and subscribe to our MLOps channels! [06:42] Back story of having Santona and Roy on the podcast [09:51] Santona's story [11:37] Optimal tag teamwork [16:53] Dealing with stakeholder needs [26:25] Having mechanisms in place [27:30] Building for data Engineers vs building for data scientists [34:50] Creating solutions for users [38:55] User experience holistic point of view [41:11] Tooling sprawl is real [42:00] LLMs reliability [45:00] Things would have loved to learn five years ago [49:46] Wrap up

Former nuclear physicist turned data scientist, Santona Tuli, shares actionable insights for data leaders implementing AI and data science. Topics include measuring unseen phenomena, challenges in data observability, impact of variables on business performance, data-driven decision making, leveraging knowledge graphs, challenges with data, and the future of AI.

Santona Tuli, head of data at Upsolver and a specialist in data culture, shares her insights on defining roles in the data field. She dives into the distinction between productivity and performance, emphasizing purposeful goal alignment. Tuli critiques the current stagnation in data management, urging a focus on outcomes rather than mere outputs. She also discusses innovative practices in data architecture and the evolving landscape of data warehousing, highlighting the importance of collaboration and curiosity in driving impactful results.