MLOps Weekly Podcast

MLOps Weekly Podcast

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Join each week as we talk to MLOps operators, practitioners, and professionals about the current state of MLOps.

Episode List

MLOps Week 32: MLOps and Feature Stores in 2025 with Ben Epstein

Feb 11th, 2025 9:18 PM

Join us for special episode of the MLOps Weekly Podcast, an hour long chat with Ben Epstein, MLE at Evolution IQ and co-host of the bi-weekly MLOps Community Virtual meetups, and Featureform CEO Simba Khadder as they reflect on the key developments in MLOps throughout 2024 and look at what trends and challenges to anticipate in 2025! The discussion will center around the integration of Large Language Models (LLMs) and their substantial influence on both MLOps and LLMOps methodologies.Key topics include:Best practices and cutting-edge architectures in MLOpsWhere MLOps meets LLMOps: key trends and areas of convergenceForecasting the MLOps landscape for 2025

MLOps Week 31: Bridging Software Engineering and MLOps with Paul lusztin of Decoding ML

Aug 1st, 2024 6:54 PM

In this episode of the "MLOps Weekly" podcast, host Simba Khadder talks with Paul Iusztin, a Senior ML and MLOps Engineer at Decoding ML, about his journey from software engineering to MLOps. They discuss the integration of software engineering principles in ML, the challenges of writing tests for ML applications, and the key differences between software and ML engineering. Paul shares insights on building scalable and reproducible MLOps platforms, emphasizing the importance of decoupling feature, training, and inference pipelines. They also explore the convergence of MLOps and LLMOps, highlighting the unique aspects of prompt engineering. The conversation underscores the importance of robust engineering practices and continuous adaptation in the rapidly evolving AI landscape.

MLOps Week 30 - From Recession to Al Boom: Venture Capital Perspectives with Gautam Krishnamurthi

Jul 3rd, 2024 10:40 PM

For the latest episode of the MLOps Weekly Podcast, join host Simba Khadder as he chats with Gautam Krishnamurthi, partner at Great Point Ventures, about the rapidly evolving world of AI and its impacts on the future of venture capital investing. They also discuss the latest trends in large language models (LLMs), venture valuations, and the impact of rising interest rates on the public markets. Gautam provides his expertise on identifying real enterprise use cases, distinguishing valuable startups amidst the noise, and the critical role of infrastructure in the machine learning landscape. Lastly, you’ll learn about the transformative power of AI, how it's reshaping industries, and what investors seek in the next wave of groundbreaking companies.

MLOps Week 29: Building the Future of ML Platforms with Ketan Umare

May 30th, 2024 8:45 PM

Join Featureform’s Founder and CEO, Simba Khadder, and Union CEO and co-founder, Ketan Umare, as they delve into Ketan’s journey, starting with leading the ETA models team at Lyft, the origins and evolution of Flyte, an ML workflow platform, and his latest venture, Union. The discussion also covers the importance of collaboration in AI, the future of traditional machine learning in the era of LLMs, and the potential disruptions in the software industry. Whether you're a data scientist, engineer, or AI enthusiast, this episode offers valuable perspectives on building scalable ML infrastructures and navigating the rapidly changing landscape of artificial intelligence.

MLOps Week 28: Featureform's CEO Breaks Down "Real-Time" Machine Learning

Mar 28th, 2024 1:16 AM

This episode of our MLOps Weekly Podcast features Simba Khadder, Featureform’s CEO, where he unravels the true meaning of “real-time” machine learning. The discussion breaks down real-time ML into three core aspects: latency, online serving, and real-time features. Simba also covers:How latency impacts the speed of ML systemsThe distinctions between online and offline modelsReal-time features and the importance of a balance between data freshness and latencyThis podcast will give listeners a better understanding of these concepts and apply them to their own ML projects.

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