AI in Production 2025
# Synthetic Data
# Meta
# Computer Vision
Synthetic data: Breaching the low data barrier in Industrial Computer Vision systems // Vasu Sharma
High-quality data collection remains one of the biggest bottlenecks in deploying AI systems at scale. Today, nearly 54% of AI projects stall at the proof-of-concept stage due to prolonged data acquisition challenges. In industries like manufacturing and industrial automation, gathering just a handful of images for object detection tasks can take six months to a year, given the complexity of these environments and the need for highly reliable models. With the rise of generative AI, synthetic data presents a transformative approach. This can accelerate data collection, reduce development cycles, and enable faster deployment of robust AI models in production.
Vasu Sharma & Qasim Wani · Mar 26th, 2025
Popular topics
# Interview
# Case Study
# Model Serving
# Machine Learning
# AI
# RAG
# FinTech
# Open Source
# Cultural Side
# Scaling
# Deployment
# Data Science
# LinkedIn
# MLOps Cycle
# Rule-bases Systems
# Artificial Intelligence
# Intelligence
# Foundational Models and MLOps
# LLM in Production
# Generative AI
Dimitrios Athanasakis · Mar 26th, 2025
Knowledge Graphs are the lingua franca of biomedicine. The talk introduces knowledge graphs and shows how modern deep learning approaches can be applied to reasoning over knowledge graphs and how this translates to actionable insights for the biomedical domain.
# Biomedicine
# Knowledge Graphs
Ron Chrisley · Mar 26th, 2025
In discussions about AI, two extreme views dominate: some claim machine consciousness is impossible, while others suggest we are already there—or that we just need larger models and more data. Both are wrong. This talk will cut through the hype and explore what consciousness actually entails, why current AI models don’t have it, and what it would take to build AI systems that might one day possess true awareness. I'll critically examine both the overblown optimism that assumes consciousness is just a scaling issue and the overly rigid skepticism that deems it forever out of reach. By breaking down the philosophical and technical dilemmas involved, we can better understand what’s missing in today’s AI and what meaningful progress in this space might look like.
# Machine conciousness
# AI awareness
Merrell Stone · Mar 26th, 2025
It’s very easy to get caught up in imagining all of the amazing things AI will one day be able to help us with. But many organizations cannot afford to invest in pipe dreams. Instead, they need to focus on what is achievable now. In this talk we'll explore a specific case study about a project designed to create a hybrid human/AI agent system and extract some high-level principles that will give participants a great place to start as they build their own agential systems.
# hybrid human
# AI
# AI Agent
Tanmay Chopra · Mar 24th, 2025
Fine-tuning isn't just about throwing more data at a model with the same pretraining loss. That’s just extended pretraining. True fine-tuning means modifying loss functions, adjusting output heads, and optimizing for real-world constraints like confidence calibration, consistency, and latency. This talk explores how misguided fine-tuning practices lead to brittle, inefficient models and demonstrates practical strategies to tailor models to production needs. We dive into when to finetune, the advantages of true finetuning (from output constraints to confidence scores to drastically lower latency) and show how finetuning can be about more than just style.
# Fine-tuning LLMs
Sebastian Kukla · Mar 24th, 2025
What should industry know before and during an Agentic AI implementation. What does it actually look like and what are the responsibilities for the consumer. For developers - what is going on in the heads of your customer and how do you coach them through it?
# Agentic
# AI in Industry
Weidong Yang · Mar 24th, 2025
Existing BI and big data solutions primarily consumes structured data, which accounts for only about 20% of enterprise information, leaving vast amounts of unstructured data underutilized. In this talk, we introduce GraphBI, which aims to address this challenge by combining GenAI, graph technology, and visual analytics to unlock the full potential of enterprise data. Technologies like Retrieval-Augmented Generation (RAG) and GraphRAG enhance summarization and Q&A but often function as black boxes, making verification difficult. In contrast, GraphBI takes a different approach: using GenAI for data pre-processing, transforming unstructured data into a graph-based format. This transparent, step-by-step workflow ensures trustworthiness and transparency of the analytics process. In this talk, we’ll walk through the GraphBI workflow, covering best practices and challenges, including: Architectural considerations for projects of varying scales; Data pre-processing, including knowledge map extraction and entity resolution; And Iterative analytics with a BI-focused graph grammar. This approach uniquely surfaces business insights by effectively incorporating all types of data.
# GenAI
# Graphs
# Visualization
Alessandro Negro · Mar 21st, 2025
This talk presents a three-step process that combines knowledge graphs with large language models (LLMs) to revolutionize how law enforcement agencies gather, analyze, and share criminal intelligence. This approach addresses critical challenges in modern policing: data silos, investigation complexity, and the need for transparent, explainable intelligence sharing.
# graphs
# llms
Ezo Saleh & Aisha Yusaf · Mar 21st, 2025
Like brilliant but untamed minds, agentic applications in production present a unique challenge: they solve problems in revolutionary ways but can be wildly unpredictable. The art of deploying these free spirits requires a delicate balance between autonomy and reliability. At Orra, we've developed a "glue layer" that acts as a skilled wrangler, ensuring reliability while preserving the agents' freedom in production environments. We'll explore its architecture including our approach to adaptive execution planning, and how we enhance domain understanding.
# Agents
# autonomy
# domain
Egor Kraev · Mar 21st, 2025
Whenever you read about taking Retrieval Augmented Generation beyond simple vector search on embeddings, graphs are almost sure to come up. But what graphs? Old-school knowledge graphs, with entities and their relationships, or document-centric graphs, with text snippets as nodes? And how do you use them to improve your retrieval? Nearest neighborhood? PageRank? Something else? I will provide an overview of what's happening in that space, including what I'm doing, and give you a tour of the different options, with their pros and cons.
# Graph
# Wise
Bassey Etim & Erica Greene · Mar 21st, 2025
In a world increasingly saturated with AI-driven applications, businesses face mounting pressure to integrate chatbots into their digital offerings. But is building a chatbot always a good idea? In this talk, we’ll channel our inner Agent Scully—skeptical but willing to investigate—as we guide you through seven critical questions that can help determine whether a chatbot is a wise investment for your company.
# chatbot
# scully
# agent