Ketan Umare

Ketan Umare, CEO at Union.ai, discusses ML orchestration vs MLOps, prioritizing features for open-source and enterprise projects, challenges of ML orchestration, building a flexible architecture, Unionverse flight and plugin models, challenges of large language models and evaluation.

In this podcast, Ketan Umare, CEO of Union and former leader of the ETA models team at Lyft, discusses the evolution of ML platforms. Topics include the importance of collaboration in AI, challenges of using Airflow in ML operations, building ML platforms with correctness and collaboration, and the future of AI workflows. Insights on navigating the changing landscape of AI and building scalable infrastructures are shared.

Ketan Umare, Co-founder and CEO of Union.ai, shares insights from his extensive experience at Lyft, Oracle, and Amazon. He discusses the limitations of Airflow in machine learning, emphasizing the need for ML-specific orchestration tools. The conversation covers the complexities of data pipelines, the importance of effective feature management, and the challenges of model drift. Ketan also highlights cloud-native solutions, security in modern engineering, and innovative programming collaborations, all while offering book recommendations that tie historical lessons to today's tech landscape.

Joining the discussion are Ketan Umare, CEO and co-founder at Union, who initiated Flyte at Lyft, and Haytham Abuelfutuh, Union's CTO, who also built Flyte there. They dive into the complexities of data orchestration in machine learning, comparing traditional tools to Flyte's innovative engine on Kubernetes. The conversation highlights the architectural design for user-friendly workflows and applications of Flyte in diverse sectors, including biotech and gaming. They also discuss the balance between open-source principles and sustainable business models.

Ketan Umare, a former engineer at Lyft, created Flyte, a groundbreaking open-source platform for workflow automation in machine learning. He discusses how Flyte integrates compute and workflow to optimize user experience. Ketan emphasizes the pivotal role of accurate fare and ETA predictions in ride-sharing. He also shares insights on transitioning from 'Better Airflow' to Flyte and the benefits of typed programming for machine learning. Additionally, he explores open-sourcing protocols and the project’s partnership with the Linux Foundation.