The Tooling Spawn in Machine Learning Engineering

The tooling sprawl is real and it's not only real in data engineering. It's the same with machine learning engineering. You got to be careful when you bring these tools into your into your day job. And recognizing this is R&D, this is not core to our business.

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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.

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https://royondata.substack.com/

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