MLOps vs ML-As-Service // Jill Chase and Manmeet Gujral // MLOps Podcast #143 clip

MLOps Coffee Sessions #143 with Jill Chase & Manmeet Gujral, Investing in the Next Generation of AI & ML. Catch the full episode at https://youtu.be/ywg5u4B7n9w! CapitalG Investors Jill Chase and Manmeet Gujral discuss the MLOps and the companies that have emerged in that space. They compare the revenue generated by these companies to that of foundational model space companies. They agree that the revenue ramp for MLOps companies tends to be slower and the go-to-market process is less efficient in the early days, but the contracts are bigger and tied to ROI. Jill and Manmeet also agree that the ML as a service or foundational model side has faster revenue growth due to clear market demand, but the differentiation between companies is important to consider. They both conclude that the go-to-market motions and end markets for both categories are different, with ML ops being more of a slow burn and ML as a service having faster revenue growth. // Abstract Investors are currently focusing on developer tooling and the foundational AI model movement, as they have seen explosive growth in this area. This podcast explores the impact of foundational models on investment thesis and the future of machine learning operations. The discussion also touches on the idea of generative AI and large language models, and their potential impact on MLOps in the next 10 years. Jill and Manmeet from Capital G share their insights on this topic. // Bio Jill Chase Jill is an investor at CapitalG where she focuses on enterprise software, with an emphasis on data infrastructure and AI/ML. Prior to joining CapitalG, Jill worked in senior startup operating roles, both as the CEO of a private equity-backed business and as the founder of a Y Combinator-backed startup. Jill graduated magna cum laude from Williams College with a dual degree in Economics and Psychology and was captain of the women’s basketball team. She came out to the West Coast to earn an MBA from the Stanford Graduate Sch