Welcoem to posts!!
in the future - u will be able to do some more stuff here,,,!! like pat catgirl- i mean um yeah... for now u can only see others's posts :c
[@neptune_ai update]
What’s new in June ↓
Product videos
► Product Update June '24: Redesigned Navigation, Run Groups, Reports, and More: learn about the latest Neptune improvements: https://youtu.be/xgv3t6bZvpE
Podcast highlights
► Learnings From Building the ML Platform at DoorDash: Hien Luu talks about building the ML Platform at DoorDash, including big data models, building platforms at the enterprise level, centralized vs. decentralized platform teams, LLM strategy, MLOps in the shadow of LLMs, and more: https://youtu.be/WvH7uqFI48M
► Navigating Vector Databases: Indexing Strategies, GPU, and More: Frank Liu discusses the basics, problems, and challenges of vector databases, including indexing strategies, segmentation, vector lengths used in production, GPU-accelerated vector databases, and potential use cases: https://youtu.be/E4rNTYN3aIg
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Extras
Catch up on the latest bonus content from the podcast episodes: https://www.youtube.com/watch?v=-dofr...
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[@neptune_ai update]
What’s new in May ↓
Product videos
► What is neptune.ai | Product Demo [Updated]: learn what Neptune is and how it works from our Head of Product, Aurimas Griciūnas: https://youtu.be/d7hG3v1K8LU
Podcast highlights
► Going Deep On Model Serving, Deploying LLMs, and Anything Production-Deployment: Chaoyu Yang discusses the MLOps stack's model serving, model registry and feature store components, online model training, LLM deployment, LLM agents, and more: https://youtu.be/oREWPxJQUEE
► Breaking Down Workflow Orchestration and Pipeline Authoring in MLOps: Adam Probs and Hamza Tahir talk about ZenML's main jobs-to-be-done and its role in end-to-end ML platforms, challenges behind testing integrations, and the future of MLOps with LLMs: https://youtu.be/UKk8EzVaUvg
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Extras
Catch up on the latest bonus content from the podcast episodes: https://www.youtube.com/watch?v=h64zf...
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[@neptune_ai update]
What’s new in April ↓
Product videos
► Create and Save Custom Table Views in Neptune: how you can customize runs table and save custom table views for future reference: https://youtu.be/tu9SGTUsWxw
► How to Control Access to ML Models in Neptune: how Neptune is structured, what user roles can be assigned to each team member, and how to manage service accounts: https://youtu.be/4GFevjFhPsU
Podcast highlights
► Learnings From Building the ML Platform at Uber (Michelangelo): Mike Del Balso shares insights on his journey from Google to Tecton, building Michelangelo, feature platforms, vector databases, and the future of the MLOps space in the world of LLMs: https://youtu.be/B5ABVupqi1U
► Building Internal ML Platform at Scout24 | How to Ship Features People Actually Need: Olalekan Elesin talks about building the ML Platform at Scout24, including problems solved, reasons for going multi-cloud, point solutions vs. end-to-end platforms, and more: https://youtu.be/2tVUwrgT0Bs
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Extras
Pick up some of the latest bonus content from the podcast episodes: https://www.youtube.com/watch?v=SSCWy...
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Experiment tracker purpose-built for foundation model training.
Monitor thousands of per-layer metrics—losses, gradients, and activations—at any scale. Visualize them with no lag and no missed spikes. Drill down into logs and debug training issues fast. Keep your model training stable while reducing wasted GPU cycles.