MLOps London January - Talks on LLMs and Model Serving Strategies

📽️ Livestream: https://youtube.com/live/moRjkkDL_n0
🧑 In Person: https://forms.gle/y23ok154qxLcZc6b7
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MLOps London is back again in January 2024 with talks on production machine learning, LLMs, DevOps, and Data Science. The plan, as usual, is to run another hybrid event so please come along in person if you're local or need an excuse to travel to London, or join us live otherwise.

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Don't forget to fill out the form above if you are coming to the in-person event. The venue needs the list of attendees to let you in.
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AGENDA:

⏱️ 6.00 pm onwards
🍺 Arrival, drinks, food, and networking

⏱️ 6.30 pm
🎤 Kick off and welcome

⏱️ 6.40 pm
🎤 The IQ of AI: Measuring Intelligence in LLMs
🙎‍♀️ Jodie Burchell - Developer Advocate in Data Science at JetBrains

Unless you’ve been living under a rock for the past 6 months, you won’t have been able to avoid being bombarded with news about the latest developments in large language models (LLMs). Much of this information quickly devolved into wild speculation about the capabilities of these models, with many claiming that they are sophisticated enough to soon replace roles as diverse as writers, designers, lawyers, doctors … and even data professionals. Others have gone further, claiming that these models are showing at least some signs of artificial general intelligence or that we’re on an inevitable path to an AI apocalypse.
In this talk, we’ll cut through the hype and delve deeply into claims of artificial general intelligence. We’ll discuss how to more systematically measure intelligence in artificial systems, and talk about where the current models stack up against this definition. By the end of this talk, you’ll see how far away we are from creating truly intelligent models, and also see some of the immediate ways we can take advantage of the capabilities of LLMs without overextending them.

⏱️ 7.25 pm
🎤 Building Open-Source LLM Applications
🧔🏻 Christopher Samiullah - CTO @ Indomitable Simulation

This talk focuses on implementing open-source Large Language Models (LLMs) in application development. It's tailored for developers who want to use LLMs with proprietary data.

• Local Development Unlocked: We begin with an introduction to llama-cpp, a tool that makes LLM inference on your machine much simpler, setting the stage for advanced local experimentation.

• Enhanced Capabilities with RAG: Practical examples of how Retrieval Augmented Generation via Llama Index can significantly elevate your models' capabilities, offering a glimpse into the possibilities of AI development.

• Performance Evaluation: We'll navigate through effective tooling and techniques to evaluate and enhance the performance of your models

• Tackling Deployment Challenges: The session will conclude with a look at the hurdles to deploying these models, providing you with a roadmap to successful implementation.

This talk is designed for developers looking to leverage open-source LLMs, offering a blend of practical guidance and innovative strategies to harness the power of AI with your own data.

⏱️ 8.15 pm
🎤 A Whirlwind Tour of ML Model Serving Strategies (Including LLMs)
🧔🏻 Ramon Perez - Developer Advocate @ Seldon

There are many recipes to serve machine learning models to end users today, and even though new ways keep popping up as time passes, some questions remain: How do we pick the appropriate serving recipe from the menu we have available, and how can we execute it as fast and efficiently as possible? In this talk, we’re going to go through a whirlwind tour of the different machine learning deployment strategies available today for both traditional ML systems and Large Language Models, and we’ll also touch on a few do’s and don’ts while we’re at it. This session will be jargonless but not buzzwordy- or meme-less.

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If you are attending in person please complete the registration form (link at the top of this description).
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