In the past 18 months, artificial intelligence has not just entered our workspaces – it has taken over. As we stand at the crossroads of innovation and automation, it’s time for a candid reflection on how AI has reshaped our professional lives, and to talk about where it’s been a game changer, where it’s falling short, and what’s about to shift dramatically in the short term. Since the release of ChatGPT in December 2022, I’ve developed a “first-reflex” to augment and accelerate nearly every task with AI. As a founder and CEO, this spans a wide array of responsibilities from fundraising, internal communications, legal, operations, product marketing, finance, and beyond. In this keynote, I’ll cover diverse use cases across all areas of business, offering a comprehensive view of AI’s impact. Join me as I sort out through this new reality and try and forecast the future of AI in our work. It’s time for a radical checkpoint. Everything’s changing fast. In some areas, AI has been a slam dunk; in others, it’s been frustrating as hell. And once a few key challenges are tackled, we’re on the cusp of a tsunami of transformation. 3 major milestones are right around the corner: top-human-level reasoning, solid memory accumulation and recall, and proper executive skills. How is this going to affect all of us?

This talk will explore ASAPP’s use of Apache Airflow to streamline and optimize our machine learning operations (MLOps). Key highlights include: Integrating with our custom Spark solution for achieving speedup, efficiency, and cost gains for generative AI transcription, summarization and intent categorization pipelines Different design patterns of integrating with efficient LLM servers - like TGI/vllm/tensor-RT for Summarization pipelines with/without Spark. An overview of batched LLM inference using Airflow as opposed to real time inference outside of it [Tentative] Possible extension of this scaffolding to Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) for fine-tuning LLMs, using Airflow as the orchestrator. Additionally, the talk will cover ASAPP’s MLOps journey with Airflow over the past few years, including an overview of our cloud infrastructure, various data backends, and sources. The primary focus will be on the machine learning workflows at ASAPP, rather than the data workflows, providing a detailed look at how Airflow enhances our MLOps processes.

Laurel provides an AI-driven timekeeping solution tailored for accounting and legal firms, automating timesheet creation by capturing digital work activities. This session highlights two notable AI projects: UTBMS Code Prediction: Leveraging small language models, this system builds new embeddings to predict work codes for legal bills with high accuracy. More details are available in our case study: https://www.laurel.ai/resources-post/enhancing-legal-and-accounting-workflows-with-ai-insights-into-work-code-prediction . Bill Creation and Narrative Generation: Utilizing Retrieval-Augmented Generation (RAG), this approach transforms users’ digital activities into fully billable entries. Additionally, we will discuss how we use Airflow for model management in these AI projects: Daily Model Retraining: We retrain our models daily Model (Re)deployment: Our Airflow DAG evaluates model performance, redeploying it if improvements are detected Cost Management: To avoid high costs associated with querying large language models frequently, our DAG utilizes RAG to efficiently summarize daily activities into a billable timesheet at day’s end.

This session reveals an experimental venture integrating OpenAI’s AI technologies with Airflow, aimed at advancing error diagnosis. Through the application of AI, our objective is to deepen the understanding of issues, provide comprehensive insights into task failures, and suggest actionable solutions, thereby augmenting the resolution process. This method seeks to not only enhance diagnostic efficiency but also to equip data engineers with AI-informed recommendations. Participants will be guided through the integration journey, illustrating how AI can refine error analysis and potentially simplify troubleshooting workflows.

Gen AI has taken the computing world by storm. As Enterprises and Startups have started to experiment with LLM applications, it has become clear that providing the right context to these LLM applications is critical. This process known as Retrieval augmented generation (RAG) relies on adding custom data to the large language model, so that the efficacy of the response can be improved. Processing custom data and integrating with Enterprise applications is a strength of Apache Airflow. This talk goes into details about a vision to enhance Apache Airflow to more intuitively support RAG, with additional capabilities and patterns. Specifically, these include the following Support for unstructured data sources such as Text, but also extending to Image, Audio, Video, and Custom sensor data LLM model invocation, including both external model services through APIs and local models using container invocation. Automatic Index Refreshing with a focus on unstructured data lifecycle management to avoid cumbersome and expensive index creation on Vector databases Templates for hallucination reduction via testing and scoping strategies

Every data team out there is being asked from their business stakeholders about Generative AI. Taking LLM centric workloads to production is not a trivial task. At the foundational level, there are a set of challenges around data delivery, data quality, and data ingestion that mirror traditional data engineering problems. Once you’re past those, there’s a set of challenges related to the underlying use case you’re trying to solve. Thankfully, because of how Airflow was already being used at these companies for data engineering and MLOps use cases, it has become the defacto orchestration layer behind many GenAI use cases for startups and Fortune 500s. This talk will be a tour of various methods, best practices, and considerations used in the Airflow community when taking GenAI use cases to production. We’ll focus on 4 primary use cases; RAG, fine tuning, resource management, and batch inference and take a walk through patterns different members in the community have used to productionize this new, exciting technology.

Artificial Intelligence is reshaping the landscape of software development. In this talk, we’ll explore the latest AI breakthroughs improving LLM capabilities for software development use cases. We’ll discuss work and ideas in the field related to Airflow, particularly around model capabilities related to Python, DSLs, and low-resource languages.

In the last few years Large Language Models (LLMs) have risen to prominence as outstanding tools capable of transforming businesses. However, bringing such solutions and models to the business-as-usual operations is not an easy task. In this session, we delve into the operationalization of generative AI applications using MLOps principles, leading to the introduction of foundation model operations (FMOps) or LLM operations using Apache Airflow. We further zoom into aspects of expected people and process mindsets, new techniques for model selection and evaluation, data privacy, and model deployment. Additionally, know how you can use the prescriptive features of Apache Airflow to aid your operational journey. Whether you are building using out of the box models (open-source or proprietary), creating new foundation models from scratch, or fine-tuning an existing model, with the structured approaches described you can effectively integrate LLMs into your operations, enhancing efficiency and productivity without causing disruptions in the cloud or on-premises.

Many organizations struggle to create a well-orchestrated AI infrastructure, using separate and disconnected platforms for data processing, model training, and inference, which slows down development and increases costs. There’s a clear need for a unified system that can handle all aspects of AI development and deployment, regardless of the size of data or models. Join our breakout session to see how our comprehensive solution simplifies the development and deployment of large language models in production. Learn how to streamline your AI operations by implementing an end-to-end ML lifecycle on your custom data, including - automated LLM fine-tuning, LLM evaluation & LLM serving and LoRA deployments