AI/ML
From Symptom Checkers to Smart Chatbots: The Role of AI in Virtual Care
Monday Apr 7 / 05:05PM BST
As digital transformation reshapes healthcare, virtual care has rapidly evolved from a niche convenience to a foundational element of modern health systems.
Andre Riberio
CTO @Healthily, PhD in Neuroimaging, 10+ Years Experience in Machine Learning, NLP, & Cloud Computing Across Healthcare, Gaming, & SaaS
The Data Backbone of LLM Systems
Wednesday Apr 9 / 02:45PM BST
Any LLM application has four dimensions you must carefully engineer: the code, data, models and prompts. Each dimension influences the other. That's why you must learn how to track and manage each. The trick is that every dimension has particularities requiring unique strategies and tooling.
Paul Iusztin
Senior ML/AI Engineer, MLOps, Founder @Decoding ML
Ecologies and Economics of Language AI in Practice
Monday Apr 7 / 11:45AM BST
Lessons learned from building language models in Africa: under strict data constraints in non-western environments.
Jade Abbott
CTO & Co-Founder @Lelapa AI, Co-Founder @Masakhane, With Over a Decade of Experience in Deploying AI Into Production
Lessons Learned From Building LinkedIn’s First Agent: Hiring Assistant
Tuesday Apr 8 / 05:05PM BST
In October 2024, we announced LinkedIn’s first agent, Hiring Assistant to a select group of LinkedIn customers.
Karthik Ramgopal
Distinguished Engineer & Tech Lead of the Product Engineering Team @LinkedIn, 15+ Years of Experience in Full-Stack Software Development
Daniel Hewlett
Principal AI Engineer & Technical Lead for AI @LinkedIn, 12+ Years of Expierence in ML and AI Engineering, Previously @Google
Lessons Learned From Shipping AI-Powered Healthcare Products
Monday Apr 7 / 10:35AM BST
This talk provides valuable insights into Sword Health's real-world experience implementing AI in healthcare, focusing on practical strategies for developing consistent, safe and reliable AI-powered healthcare solutions.
Clara Matos
Head of AI Engineering @Sword Health, Focused on Building and Scaling Machine Learning Systems
Achieving Precision in AI: Retrieving the Right Data Using AI Agents
Wednesday Apr 9 / 11:45AM BST
In the race to harness the power of generative AI, organizations are discovering a hidden challenge: precision.
Adi Polak
Director, Advocacy and Developer Experience Engineering @Confluent, Author of "Scaling Machine Learning with Spark" and "High Performance Spark 2nd Edition"
Securing AI Copilots: Strategies and Practices for Protecting Data
Tuesday Apr 8 / 03:55PM BST
The data behind AI copilots is not only their most critical asset but also a key strategic consideration for enterprises and SMBs alike.
Andra Lezza
Principal Application Security Specialist @Sage, 10+ Years of Experience Building AppSec Programs, OWASP London Chapter Leader
Building Embedding Models for Large-Scale Real-World Applications
Tuesday Apr 8 / 03:55PM BST
Embedding models are at the core of search, recommendation, and retrieval-augmented generation (RAG) systems, transforming data into meaningful representations.
Sahil Dua
Senior Software Engineer, Machine Learning @Google, Stanford AI, Co-Author of “The Kubernetes Workshop”, Open-Source Enthusiast
Foundation Models for Ranking: Challenges, Successes, and Lessons Learned
Tuesday Apr 8 / 02:45PM BST
Recommender systems are an integral part of most products nowadays and are often a key driver of discovery for users of the product.
Moumita Bhattacharya
Senior Research Scientist @Netflix, Previously @Etsy
AI for Food Image Generation in Production: How & Why
Tuesday Apr 8 / 01:35PM BST
In this talk, we will conduct a technical overview of a client-facing Food Image Generation solution developed at Delivery Hero.
Iaroslav Amerkhanov
Senior Data Scientist @Delivery Hero, Founder of T4lky, Creator & Host of EPAM Podcast, Speaker