How mlctl Helps Intuit's Workflow // Srivathsan Canchi // Coffee Sessions # 50 short clip

Coffee Sessions #50 with Alex Chung and Srivathsan Canchi, Creating MLOps Standards.

"That's where the platform team comes in and says, 'Hey, you use the orchestration tool of your choice but we would like to make sure that these certain basic tenets are followed.' That's where mlctl comes in and mlctl is deeply integrated into both the Airflow and the Kubeflow pipelines and it provides consistent interphase no matter where you're calling it from." - Srivathsan Canchi

// Abstract
With the explosion in tools and opinionated frameworks for machine learning, it's very hard to define standards and best practices for MLOps and ML platforms. Based on their building AWS SageMaker and Intuit's ML Platform respectively, Alex Chung and Srivathsan Canchi talk with Demetrios and Vishnu about their experience navigating "tooling sprawl". They discuss their efforts to solve this problem organizationally with Social Good Technologies and technically with mlctl, the control plane for MLOps.

// Bio
Srivathsan leads the machine learning platform engineering team at Intuit. The ML platform includes real-time distributed featurization, scoring, and feedback loops. He has a breadth of experience building high scale mission-critical platforms. Srivathsan also has extensive experience with K8s at Intuit and previously at eBay, where his team was responsible for building a PaaS on top of K8s and OpenStack.

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Alex on LinkedIn: https://linkedin.com/in/alex-chung-gsd
Connect with Sri on LinkedIn: https://www.linkedin.com/in/srivathsancanchi/

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