38 episodes
ML Platform Podcast neptune.ai
-
- Technology
-
-
5.0 • 1 Rating
-
Get behind-the-scenes insights into the world of internal ML platforms and MLOps stack components with Piotr Niedźwiedź and Aurimas Griciūnas in their show, where together with ML platform professionals, they discuss design choices, best practices, and real-world solutions to MLOps challenges.
Brought to you by neptune.ai.
-
Learnings From Building the ML Platform at DoorDash With Hien Luu
On this episode of the ML Platform Podcast, Hien Luu talks about building the ML Platform at DoorDash, including big data models, building platforms at the enterprise level, centralized vs. decentralized ML platform teams, LLM strategy, MLOps in the shadow of LLMs, and more.
-
Breaking Down Workflow Orchestration and Pipeline Authoring in MLOps
On this episode of the ML Platform Podcast, Adam Probs and Hamza Tahir discuss workflow orchestration and pipeline authoring in MLOps, focusing on ZenML's capabilities. They cover the main jobs-to-be-done, challenges behind testing integrations, ZenML’s role in end-to-end ML platforms, ZenML Cloud, the future of MLOps with LLMs, and more.
-
Going Deep On Model Serving, Deploying LLMs and Anything Production-Deployment
On this episode of the ML Platform Podcast, Chaoyu Yang discusses the MLOps stack's model serving, model registry and feature store components, online model training, large language model deployment, LLM agents, and more.
-
Building Internal ML Platform at Scout24: How to Ship Features that People Actually Need
On this episode of the ML Platform Podcast, we have Olalekan Elesin as our guest. Olalekan shares insights on building the ML Platform at Scout24, including problems to be solved, reasons for going multi-cloud, point solutions vs. end-to-end platforms, data platforms vs. ML platforms, iterative product development approach, and more.
-
Building ML Platform at Uber, Feature Stores, Vector Databases, and Real-time Feature Management
On this episode of the ML Platform Podcast, Mike Del Balso is our guest. Mike shares insights on his journey from Google to Tecton, building the ML platform (Michelangelo) at Uber, feature platforms, vector databases, and the future of the MLOps space in the world of foundational models.
-
Year in Review: LLMs & LLMOps, State of MLOps, and What's Next in 2024
On this special, end-of-the-year episode of the ML Platform Podcast, Piotr Niedzwiedzi and Aurimas Griciūnas discuss the state of MLOps and LLMOps, the impact of LLMs on layoffs and ML team composition, unsolved LLM challenges, use cases that don’t align with LLMs, MLOps and LLMOps predictions for 2024, and more.
With LLMs and AI being the center of discussion we also decided to experiment with generative AI for this video. Hope you don’t mind the experimentation.
Customer Reviews
Top Podcasts In Technology
You Might Also Like