// 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

Alex Chung

Alex is a former Senior Product Manager at AWS Sagemaker and an ML Data Strategy and Ops lead at Facebook. He's passionate about the interoperability of MLOps tooling for enterprises as an avenue to accelerate the industry.

Srivathsan Canchi

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.

--------------- ✌️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

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/

Timestamps:

[00:00] Introduction to Alex Chung and Srivathsan Canchi

[01:36] Alex's background in tech

[03:07] Srivathsan's background in tech

[04:36] What is SGT?

[05:53] 3 Categories of SGT

           1. Education

           2. Standardization

           3. Orchestration  

[07:00] Standardization is desirable

[13:03] Perspective from both sides  

[13:39] Profile breakdown of Standardization

[17:20] Importance of Standardization in enterprise

[21:02] Tooling sprawl

[24:04] Standardizing the different interfaces between MLOps tools

[31:54] mlctl

[33:35] mlctl's future

[38:38] How mlctl helps the workflow of Intuit

[41:00] CIGS evolve the different spaces