09:07

Entity Resolved Knowledge Graphs | Paco Nathan

11:57

Less is not more: How to serve more models efficiently | Julia Turc

15:05

Enterprise AI Governance: A Comprehensive Playbook | Ian Eisenberg

14:22

Designing data quality for AI-usecase | Mona Rakibe

09:57

Why You Cannot Ignore Foundational Data Systems When Building Reliable AI Solutions | Vishakha Gupta

07:44

AI-Driven Code Generation and Website Builders | Patryk Pijanowski

09:38

Building a Product Optimization Loop for Your LLM Features | Jeremy Silva

11:04

A Systematic Approach to Improve Your AI Powered Applications | Karthik Kalyanaraman

15:18

THE ONLY REAL MOAT FOR GENERATIVE AI: TRUSTED DATA | Barr Moses

19:49

Evaluating LLM Tool use: A Survey | Rick Lamers

10:49

Calibrating the Mosaic Evaluation Gauntlet | Theresa Barton

16:32

Building Robust and Trustworthy Gen AI Products: A Playbook | Faizaan Charania

10:24

Beyond Benchmarks: Measuring Success for Your AI Initiatives | Salma Mayorquin

19:26

Building Safer AI: Balancing Data Privacy with Innovation | STEPHANIE KIRMER

19:37

AIOps, MLOps, DevOps, Ops: Enduring Principles and Practices | Charles Frye

20:42

GraphRAG: Enriching RAG conversations with knowledge graphs | Kirk Marple

14:50

The Power of Small Language Models: Compact Designs for Big Impact | Joshua Alphonse

28:33

FROM PREDICTIVE TO GENERATIVE: UBER'S JOURNEY | KAI WANG, RAAJAY VISWANATHAN

24:03

Generating The Invisible: Capturing and Generating Edge-cases in Autonomous Driving | Felix Heide

33:42

EIGHTY-THOUSAND POUND ROBOTS: AI DEVELOPMENT & DEPLOYMENT AT KODIAK SPEED | Collin Otis

34:51

TO RAG OR NOT TO RAG? | AMR AWADALLAH

19:52

HOW TO TAKE CONTROL OF YOUR RAG RESULTS | Daniel Svonava

21:50

OPEN MODEL AND ITS CURATION OVER KUBERNETES | Cindy Xing

09:12

MITIGATING HALLUCINATIONS AND INAPPROPRIATE RESPONSES IN RAG APPLICATIONS | Alon Gubkin

23:39

GROWING RELIABLE ML/AI SYSTEMS THROUGH FREEDOM AND RESPONSIBILITY | Savin Goyal

16:30

LEARNING FROM OUR PAST MISTAKES, OR HOW TO CONTAINERIZE THE AI PIPELINE | Solomon Hykes

13:54

UNLOCKING TRUST: ENHANCING AI QUALITY FOR CONTENT UNDERSTANDING IN VISUAL DATA

21:00

SELF IMPROVING RAG | Chang She

16:12

FIRESIDE CHAT: CALIFORNIA AND THE FIGHT OVER AI REGULATION

27:32

HUNTING FOR QUALITY SIGNALS: MEASURE DATA QUALITY & IDENTIFY GENERATIVE AI FAILURE MODES

35:40

BALANCING SPEED AND SAFETY

27:15

PANEL: DATA QUALITY = QUALITY AI

30:34

FIRESIDE CHAT: VISION AND STRATEGIES FOR ATTRACTING & DRIVING AI TALENTS IN HIGH GROWTH

30:04

OVERCOMING BIAS IN COMPUTER VISION AND VOICE RECOGNITION

35:39

A BLUEPRINT FOR SCALABLE & RELIABLE ENTERPRISE AI/ML SYSTEMS

23:58

DO RE MI FOR TRAINING METRICS: START AT THE BEGINNING | Todd Underwood

23:15

EVALUATION OF ML SYSTEMS IN THE REAL WORLD | Mohamed El-Geish

18:17

EVALUATING EVALUATIONS | Linus Lee

23:56

INTEGRATING LLMS INTO PRODUCTS | Emmanuel Ameisen

16:49

BUILDING ADVANCED QUESTION-ANSWERING AGENTS OVER COMPLEX DATA | JERRY LIU

23:38

THE NEW AI STACK WITH FOUNDATION MODELS | Chip Huyen

32:59

THE DOLLARS AND CENTS BEHIND THE AI VC BOOM

22:24

NEW QUALITY STANDARDS FOR AUTONOMOUS DRIVING WITH MOHAMED ELGENDY AND MOHAMED ELSHENAWY