#mlops #ai #llm #machinelearning

MLOps.community · Beginner ·🧠 Large Language Models ·2y ago

Key Takeaways

The video discusses the challenges of implementing Large Language Models (LLMs) in product development, focusing on security concerns and collaborative measures against attacks, with Phillip Carter sharing lessons learned from production experiences.

Full Transcript

what we learned is that once you're in production the fastest thing you can do is just literally look at the inputs and outputs of like this is what users are doing over the past 24 hours these were all the user inputs these were all of the llm outputs that came from that output and then this is the result of our like parsing and validation system that tries to take that llm output and and you know we parse it and there's like a potential kinds of errors that can happen there and then we try to execute it against our querying engine and we learned that it's a lot faster to just work with that data directly because now we're no longer in a hypothetical World we're like all right this is what people are literally trying right now

Original Description

MLOps Coffee Sessions #170 with Phillip Carter, All the Hard Stuff with LLMs in Product Development. Link to the full episode: https://youtu.be/DZgXln3v85s // Abstract Delve into challenges in implementing LLMs, such as security concerns and collaborative measures against attacks. Emphasize the role of ML engineers and product managers in successful implementation. Explore identifying leading indicators and measuring ROI for impactful AI initiatives.
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Playlist

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This video teaches the importance of considering security concerns and collaborative measures when implementing LLMs in product development, and how to work with production data to improve the development process. It highlights the challenges of LLM implementation and provides lessons learned from production experiences. By watching this video, viewers can gain practical insights into LLM engineering and MLOps.

Key Takeaways
  1. Identify security concerns in LLM implementation
  2. Develop collaborative measures against attacks
  3. Work with production data to improve development
  4. Parse and validate LLM outputs
  5. Execute LLM outputs against a querying engine
💡 Working with production data directly can be faster and more effective than hypothetical scenarios when implementing LLMs.

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