Exploring MLOps and LLMOps: Architectures and Best Practices
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Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and over 60% of the Fortune 500 — rely on Databricks to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified Data Intelligence Platform that includes Agent Bricks, Genie, Lakebase, Lakeflow, Lakehouse, and Unity Catalog.
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Video Description
This session offers a detailed look at the architectures involved in Machine Learning Operations (MLOps) and Large Language Model Operations (LLMOps). It's designed for those interested in the technical specifics and practical applications of MLOps and LLMOps, including the key components and workflows that define these fields. Attendees will learn about: The core architectures used in MLOps and LLMOps. The various elements involved in these operations, such as data management, model training, deployment, and monitoring. Strategies for implementing effective MLOps and LLMOps in your projects, with a focus on improving efficiency and model performance. Talk By: Arpit Jasapara, Software Engineer, Databricks ; Joseph Bradley, Principal Product Specialist, Databricks ; Yinxi Zhang, Staff Data Scientist, Databricks Here's more to explore: LLM Compact Guide: https://dbricks.co/43WuQyb Big Book of MLOps: https://dbricks.co/3r0Pqiz Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/data… Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc
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