AI in Production - Event | MLOps Community

Welcome - AI in Production

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Anatomy of a Software 3.0 Company

If software 2.0 was about designing data collection for neural network training, software 3.0 is about manipulating foundation models at a system level to create great end-user experiences. AI-native applications are “GPT wrappers” the way SaaS companies are database wrappers. This talk discusses the huge design space for software 3.0 applications and explains Conviction’s framework for value, defensibility and strategy in specifically assessing these companies.

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From Past Lessons to Future Horizons: Building the Next Generation of Reliable AI

In this talk, Shreya will share a candid look back at a year dedicated to developing reliable AI tools in the open-source community. The talk will explore which tools and techniques have proven effective and which ones have not, providing valuable insights from real-world experiences. Additionally, Shreya will offer predictions on the future of AI tooling, identifying emerging trends and potential breakthroughs. This presentation is designed for anyone interested in the practical aspects of AI development and the evolving landscape of open-source technology, offering both reflections on past lessons and forward-looking perspectives.

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Productionizing Health Insurance Appeal Generation

This talk will cover how we fine-tuned a model to generate health insurance appeals. If you've ever gotten a health insurance denial & just kind of given up hopefully the topic speaks to you. Even if you have not, come to learn about our adventures in using different cloud resources for fine-tuning and finally an on-prem Kubernetes based deployment in Fremont, CA including when the graphics cards would not fit in the servers.

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Navigating through Retrieval Evaluation to demystify LLM Wonderland

This session talks about the pivotal role of retrieval evaluation in Language Model (LLM)-based applications like RAG, emphasizing its direct impact on the quality of responses generated. We explore the correlation between retrieval accuracy and answer quality, highlighting the significance of meticulous evaluation methodologies.

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It is possible to build KGs with LMs through prompt engineering. But are we boiling the ocean? Can we improve the quality of the generated graph elements by using - dare I say it - SLMs (small language models)?

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Explaining ChatGPT to Anyone in 10 Minutes

Over the past few years, we have witnessed a rapid evolution of generative large language models (LLMs), culminating in the creation of unprecedented tools like ChatGPT. Generative AI has now become a popular topic among both researchers and the general public. Now more than ever before, it is important that researchers and engineers (i.e., those building the technology) develop an ability to communicate the nuances of their creations to others. A failure to communicate the technical aspects of AI in an understandable and accessible manner could lead to widespread public scepticism (e.g., research on nuclear energy went down a comparable path) or the enactment of overly-restrictive legislation that hinders forward progress in our field. Within this talk, we will take a small step towards solving these issues by proposing and outlining a simple, three-part framework for understanding and explaining generative LLMs.

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Lots of companies are investing time and money in LLMs, some even have customer-facing applications, but what about some common sense? Impact assessment | Risk assessment | Maturity assessment.

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Productionizing AI: How to Think From the End

As builders, engineers, and creators, we are often thinking about starting the full life-cycle of a machine learning or AI project from gathering data, cleaning the data, and training and evaluating a model. But what about the experiential qualities of an AI product that we want our user to be able to experience on the front end? Join me to learn about the foundational questions I ask myself and my team while building products that incorporate LLMs.

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Evaluating Large Language Models (LLMs) for Production

In the rapidly evolving field of natural language processing, the evaluation of Large Language Models (LLMs) has become a critical area of focus. We will explore the importance of a robust evaluation strategy for LLMs and the challenges associated with traditional metrics such as ROUGE and BLEU. We will conclude the talk with some nontraditional such as correctness, faithfulness, and freshness metrics that are becoming increasingly important in the evaluation of LLMs.

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Vision Pipelines in Production: Serving & Optimisations

We will discuss how can we go from developing a solution to production in context of vision models, exploring fine-tuning LORAs, upscaling pipelines, constraints based generations, and step-by-step improving overall performance & quality for a production ready service.

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Charting LLMOps Odyssey: challenges and adaptations

In this presentation, we navigates the iterative development of Large Language Model (LLM) applications and the intricacies of LLMOps design. We emphasize the importance of anchoring LLM development in practical business use cases and a deep understanding of your own data. Continuous Integration and Continuous Deployment (CI/CD) should be a core component for LLM pipeline deployment, just like in Machine Learning Operations (MLOps). However, the unique challenges posed by LLMs include addressing data security, API governance, the imperative need for GPU infrastructure in inference, integration with external vector databases, and the absence of clear evaluation rubrics. Join us as we illuminate strategies to overcome these challenges and make strategic adaptations. Our journey includes reference architectures for the seamless productionization of RAGs on the Databricks Lakehouse platform.

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Evaluating Language Models

I'll be talking about the challenges of evaluating language models, as well as how to address them, what metrics you can use, and datasets available. Discuss difficulties of continuous evaluation in production and common pitfalls.

Takeaways: A call to action to contribute to public evaluation datasets and a more concerted effort from the community to reduce harmful bias.

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Helix - Fine Tuning for Llamas

A quick run down of Helix and how it helps you to fine tune text and image AI all using the latest open source models. Kai will discuss some of the issues that cropped up when creating and running a fune tuning as a service platform.

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Let's Build a Website in 10 minutes with GitHub Copilot

GitHub Copilot based on GPT is truly a game changer when it comes to automating the code generation, thus boosting developer productivity by more than 100%.In this session, you will have a chance to learn what GitHub Copilot is, and you will build a console web app in about 10 minutes with GitHub Copilot!

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