AI in Production 2025
# adobe
# Data Management
Intentional Arrangement: From Digital Hellscape to Information Nirvana // Jessica Talisman
Humans have catalogued information for more than 3,000 years, a practice that has evolved as technology has advanced. The Library and Information Science discipline is responsible for building systems for organizing data, to serve our physical and digital knowledge domains. How have librarians sustained analog and digital repositories? Intentional arrangement. With the current wave of artificial intelligence (AI), many organizations are struggling from poor data management. Welcome to the digital hellscape, rife with dirty data that is un-curated, unstructured, undefined and ambiguous. To emerge from this hellscape, look towards the librarians, who can show you the way. Controlled vocabularies, taxonomies, thesauri, ontologies and knowledge graphs have all emerged from the librarian’s toolbox. In kind, AI performance is optimized when trained on the same, intentionally arranged, structured data. Harmonious data ecosystems, optimized for human and machine, is our information nirvana and can be achieved with intentional arrangement.Humans have catalogued information for more than 3,000 years, a practice that has evolved as technology has advanced. The Library and Information Science discipline is responsible for building systems for organizing data, to serve our physical and digital knowledge domains. How have librarians sustained analog and digital repositories? Intentional arrangement. With the current wave of artificial intelligence (AI), many organizations are struggling from poor data management. Welcome to the digital hellscape, rife with dirty data that is un-curated, unstructured, undefined and ambiguous. To emerge from this hellscape, look towards the librarians, who can show you the way. Controlled vocabularies, taxonomies, thesauri, ontologies and knowledge graphs have all emerged from the librarian’s toolbox. In kind, AI performance is optimized when trained on the same, intentionally arranged, structured data. Harmonious data ecosystems, optimized for human and machine, is our information nirvana and can be achieved with intentional arrangement.
Jessica Talisman · Mar 17th, 2025
Popular topics
# LLMs
# RAG
# Interview
# AI
# Machine Learning
# Case Study
# Model Serving
# Deployment
# Artificial Intelligence
# Generative AI
# LLM
# AI Agents
# MLops
# Monitoring
# FinTech
# Open Source
# Cultural Side
# Scaling
# Kubernetes
# Data Science
Hala Nelson · Mar 17th, 2025
Because historically Data and AI have evolved within relatively separate communities, their capabilities, benefits, and adoption strategies are valued differently by different work teams and investment decision makers. Many of us are currently attempting to harness the power of AI technologies at large complex organizations, or small ones for that matter. Initiatives span across a wide range of interests within an organization: AI specialists, data engineers, IT departments, strategists, ethicists, executives, and the people on the ground. How does an implementation team guide a 32,000 person institution to optimally adopt AI within the very short time attention span of an executive who wants an immediate return on investment? Is striking a deal with Microsoft Co-Pilot or OpenAI enough? Can resources be justified for the ambitious goal to create a digital twin of the processes and systems of an entire organization where AI can be applied to drive efficiencies and improvements? Are the required technologies, expertise, and resources available? In this talk I will present my experience on what it takes to move from aspiration to an implementable reality- from math to data to strategy to people to everything in between. We’ll also try to answer the question on everyone’s mind: will we eventually succeed, or will all our efforts end up in the wasteland of failed projects, efforts, funding, and time?
# DATA
# Processes
# AI
Vaibhav Gupta · Mar 17th, 2025
Deploying Large Language Models (LLMs) in production brings a host of challenges well beyond prompt engineering. Once they’re live, the smallest oversight—like a malformed API call or unexpected user input—can cause failures you never saw coming. In this talk, Vaibhav Gupta will share proven strategies and practical tooling to keep LLMs robust in real-world environments. You’ll learn about structured prompting, dynamic routing with fallback handlers, and data-driven guardrails—all aimed at catching errors before they break your application. You’ll also hear why naïve use of JSON can reduce a model’s accuracy, and discover when it’s wise to push back on standard serialization in favor of more flexible output formats. Whether you’re processing 100+ page bank statements, analyzing user queries, or summarizing critical healthcare data, you’ll not only understand how to keep LLMs from “breaking,” but also how to design AI-driven solutions that scale gracefully alongside evolving user needs.
# BAML
# LLMS
Sishi Long · Mar 17th, 2025
A well-crafted prompt is essential for obtaining accurate and relevant outputs from LLMs (Large Language Models). Prompt design enables users new to machine learning to control model output with minimal overhead. To facilitate rapid iteration and experimentation of LLMs at Uber, there was a need for centralization to seamlessly construct prompt templates, manage them, and execute them against various underlying LLMs to take advantage of LLM support tasks. To meet these needs, we built a prompt engineering toolkit that offers standard strategies that encourage prompt engineers to develop well-crafted prompt templates. The centralized prompt engineering toolkit enables the creation of effective prompts with system instructions, dynamic contextualization, massive batch offline generation (LLM inference), and evaluation of prompt responses. Furthermore, there’s a need for version control, collaboration, and robust safety measures (hallucination checks, standardized evaluation framework, and a safety policy) to ensure responsible AI usage.
Afshaan Mazagonwalla · Mar 17th, 2025
Building a GenAI chatbot for millions of users? This session reveals the secret sauce: best practices in LLM orchestration, agentic workflows, and grounded responses, all while prioritizing brand safety. Learn key architectural decisions for balancing latency and quality, and discover strategies for scaling to production.
# Google
# ChatBot
Aditya Gautam · Mar 17th, 2025
The advent of Large Language Models (LLMs) has significantly transformed the landscape of recommendation systems, marking a shift from traditional discriminative approaches to more generative paradigms. This transition has not only enhanced the performance of recommendation systems but also introduced a new set of challenges that need to be addressed. LLMs has several practical use-cases in modern recommendation systems, including retrieval, ranking, embedding generation for users and items in diverse spaces, harmful content detection, user history representation, and interest exploration and exploitation. However, integrating LLMs into recommendation systems is not without its hurdles. On the algorithmic front, issues such as bias, integrity, explainability, freshness, cold start, and the integration with discriminative models pose significant challenges. Additionally, there are numerous production deployment and development challenges, including training, inference, cost management, optimal resource utilization, latency, and monitoring. Beyond these, there are unforeseen issues that often remain hidden during A/B testing but become apparent once the model is deployed in a production environment. These include impact dilution, discrepancies between pre-test and backtest results, and model dependency, all of which can affect the overall effectiveness and reliability of the recommendation system. Addressing these challenges is crucial for harnessing the full potential of LLMs in recommendation systems.
# LLM
# AI Systems
# Meta
+1
Monika Podsiadlo, Chiara Caratelli, Rex Harris & 1 more speaker · Mar 14th, 2025
Voice AI agents sound great in theory but in practice? They come with a whole new set of challenges—latency, accuracy, real-time processing, handling ambiguity, and making them feel actually useful. This panel digs into the gritty realities of building and scaling voice agents that don’t just talk but truly deliver.
An MLOps Community Production sponsored by Humanloop & Rafay
# Voice AI
# AI Agents
Amy Hodler & David Hughes · Mar 14th, 2025
Integrating graphs with RAG processes has demonstrated clear benefits in improving the accuracy and explainability of GenAI. Graphs enhance the semantic capability of vector searches with more global enrichment and domain-specific grounding. The increasing adoption of GraphRAG reflects its value as shown in numerous blogs, GitHub projects, research, and formal articles. But graphs are fiddly and an iterative approach is almost always required. Today’s GraphRAG approaches focus on text and lexical graphs. However, non-text data is dense with latent signals that we currently just toss out. Integrating information from images and audio would prove an extremely rich layer of context to agentic workflows. The next major advance in GraphRAG, will be incorporating all the semantic signals latent in images and audio. This session focuses on multimodal GraphRAG or mmGraphRAG. mmGraphRAG represents a transformative step forward in bridging multimodal data through innovative search and analytics frameworks. We’ll demonstrate how integrating the semantic richness of images and text with the contextual reasoning power of graphs, mmGraphRAG provides a comprehensive, explainable, and actionable approach to solving complex data challenges. You’ll learn how to incorporate images into GraphRAG and customize graph schemas as well as search that combines visual elements. We’ll walk you through the high-level architecture and the use of associative intelligence to transform search and analytics. Notebooks that illustrate creating embeddings and creating a multimodal graph from image decomposition will be provided so you can explore how mmGraphRAG can be applied to specific domains. We’ll also leave time to discuss the implications of adding graph pattern analytics to images.
# RAG
# GenAI
# GitHub
Samuel Partee · Mar 14th, 2025
This talk is about making AI agents truly useful by fixing how we handle authorization. Right now, we depend on API keys and static tokens stored in environment variables that tie actions to single users, which isn't flexible or secure for bigger operations. I'll cover why this holds us back from letting AI do important tasks, like sending emails or managing sensitive data, autonomously. We'll explore simple ways to update these systems, so AI can work for us without constant human intervention. This is all about moving beyond flashy demos to real-world, impactful AI applications.
# AI Agents
# AI Applications
# Arcade.dev
Paco Nathan · Mar 14th, 2025
The Dark Web: an estimated $3T USD flows annually through shell corps and tax havens worldwide -- serving as the perpetua mobilia for oligarchs, funding illegal weapons transfers, mercenaries, human trafficking at scale, anti-democracy campaigns, cyber attacks at global scale, even illegal fishing fleets. Tendrils of kleptocracy extend through the heart of London, reaching into many of the VC firms in Silicon Valley, and now into the White House. The people who "catch bad guys" – investigative journalists, regulators, gov agencies – leverage AI apps to contend with the overwhelming data volumes. Few of those who do "bad guy hunting" get to speak at tech conferences. However, our team provides core technology for this work, and we can use open source, open models, and open data to illustrate. How technology gets used to stick the moves of the world's worst organized crime, how to fight against the oligarchs who use complex networks to hide their grift. This talk explores known cases, tradecraft employed, and open data sources for fighting against kleptocracy. Moreover, we'll look at where AI and Data professionals are very much needed, where you can get involved.
# Dark Web
# Cyber Attacks
# Senzing
Joshua Alphonse · Mar 14th, 2025
As LLMs become widespread, enterprises build AI apps, often exposing sensitive data to centralized service providers and getting locked into their models. While smaller, specialized models can cut costs by up to 70%. In this talk, I'll quickly take you over what goes into building production-ready SLMs.
# LLM
# AI Apps
# PremAI
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