Conferences / MLOps World (Machine Learning in Production)
4th Annual MLOps World Conference & Expo 2023
Oct 25–26, 2023 · Renaissance Austin Hotel, Austin, TX, United States
- Conference
- MLOps World (Machine Learning in Production) Data science & ML
- Dates
- Oct 25, 2023 – Oct 26, 2023
- Venue
- Renaissance Austin Hotel
- Place
- Austin, TX, United States
- Format
- In person
- Status
- Past
- Attendance
- —
- Website
- mlopsworld.com
- Recorded
- 31 sponsors · 61 speaker appearances
- Notes
- First Austin edition (Renaissance Austin Hotel, 9721 Arboretum Blvd). FAQ: held in person, talks not streamed; some workshops were run virtually (role 'workshop (virtual)'). Co-located with the Generative AI Summit. Homepage: 'Tickets starting at only $299' (currency not stated, so not recorded as a price). Speakers: full list from the speakers page (Nov 2023 snapshot). Sponsor names derived from logo links (no alt text).
Ticket prices
No ticket prices recorded
Sponsors 31
Platinum 1
- Aporia · Platinum Sponsor
Gold 8
- Canonical · Gold Sponsors
- Featureform · Gold Sponsors
- Iterative · Gold Sponsors
- Lightning AI · Gold Sponsors
- Neptune · Gold Sponsors
- Seldon · Gold Sponsors
- Tenstorrent · Gold Sponsors
- VESSL AI · Gold Sponsors
Silver 3
- Genesis Cloud · Silver Sponsors
- Union.ai · Silver Sponsors
- Valohai · Silver Sponsors
Bronze 5
Startup 6
Community 4
- AITX Community · Community Partners
- DataTalks.Club · Community Partners
- LLM Eco Impact (lu.ma) · Community Partners
- Women in AI & Robotics · Community Partners
Other 4
- Deepgram · Exhibitors
- Elastic · Exhibitors
- Outerbounds · Exhibitors
- Symbl.ai · Exhibitors
Speakers and talks 61
| Speaker | Company | Role | Talk | Links |
|---|---|---|---|---|
| Aayush Mudgal | Speaker | Evolution of ML Training and Serving Infrastructure at Pinterest | — | |
| Aishwarya Reganti | Amazon Web Services | Speaker | MLOps for Graph-Based Recommender Systems: Orchestrating Intelligent Connections | — |
| Alon Gubkin | Aporia | Workshop (virtual) | Spend Less Time Troubleshooting ML Production Issues | — |
| Andreea Munteanu | Canonical | Speaker | MLOps on Highly Sensitive Data - Strict Confinement, Confidential Computing and Tokenization Protecting Privacy | — |
| Andreea Munteanu | Canonical | Workshop | Build Your Own ChatGPT with Open Source Tooling | — |
| Andreea Munteanu | Canonical | Lightning | Generative AI: The Open Source Way | — |
| Aniket Maurya | Lightning AI | Workshop (virtual) | Finetuning a Large Language Model on A Custom Dataset | — |
| Anouk Dutree | UbiOps | Workshop (virtual) | Deploying Generative AI Models: Best Practices and An Interactive Example | — |
| Aurimas Griciūnas | Neptune | Lightning | Building an end-to-end MLOps Pipeline | — |
| Chris Alexiuk | AI Makerspace | Workshop | Retrieval Augmented Generation (RAG) with LangChain: “ChatGPT for Your Data” with Open-Source Tools | — |
| D. Sculley | Kaggle | Speaker | Is It Too Much to Ask for A Stable Baseline? | — |
| Dan Shiebler | Abnormal Security | Workshop (virtual) | How to Design and Build Resilient Machine Learning Systems | — |
| Daniel Lenton | Ivy | Speakers corner | Which Compilers Are Best for LLMs? | — |
| David Bennett | Tenstorrent | Speaker | Building Computers for AI | — |
| David Talby | John Snow Labs | Workshop (virtual) | Applying Responsible AI with the Open-Source LangTest Library | — |
| Dmitry Petrov | DVC | Speakers corner | Data Versioning in Generative AI: A Pathway to Cost-Effective ML | — |
| Dr. Greg Loughnane | AI Makerspace | Workshop | Retrieval Augmented Generation (RAG) with LangChain: “ChatGPT for Your Data” with Open-Source Tools | — |
| Dr. Ilyas Iyoob | The University of Texas at Austin | Speaker | Lessons Learned from Implementing GenAI at Large Enterprises | — |
| Eddie Mattia | Outerbounds | Workshop (virtual) | LLMs in Practice: A Guide to Recent Techniques and Trends | — |
| Eddie Mattia | Outerbounds | Lightning | LLMs from Hallucinations to Relevant Responses | — |
| Fabiana Clemente | YData | Workshop (virtual) | Synthetic Data: Generative AI for Enhanced Data Quality in the Era of Foundational Models | — |
| Goku Mohandas | Anyscale | Speaker | End-to-end Production Machine Learning Workflows | — |
| Gon Rappaport | Aporia | Lightning | The Evolution of ML Monitoring in Production: From ML 1.0 to LLMs | — |
| Greg Kuhlmann | Sumatra | Workshop (virtual) | Learning from Extremes: What Fraud-Fighting at Scale Can Teach Us About MLOps Across Domains | — |
| Guanghua Shu | Instacart | Workshop (virtual) | Lessons Learned: The Journey to Real-Time Machine Learning at Instacart | — |
| Hagay Lupesco | MosaicML | Speaker | Training Large Language Models: Lessons from The Trenches | — |
| Hien Luu | DoorDash | Speaker | LLMOps: An Emerging Stack to Productionalize LLM Applications | — |
| Ian Schweer | Riot Games | Speaker | Amumu Brain; How League of Legends Uses Machine Learning an Applied Data Science | — |
| Jonas Mueller | Cleanlab | Speaker | Operationalizing Data-Centric Ai: Practical Algorithms + Software to Quickly Improve ML Datasets | — |
| Jose Nicholas Francisco | Deepgram | Lightning | LLMs, Big Data, and Audio: Breaching an Untapped Gold Mine | — |
| Kumaran Ponnambalam | Cisco | Speaker | Using Scouter Models to Monitor Model Drift - A Novel Approach | — |
| Liran Hason | Aporia | Speaker | Removing the Roadblocks to Build Great GenAI Products | — |
| Maciej Mazur | Canonical | Speaker | MLOps on Highly Sensitive Data - Strict Confinement, Confidential Computing and Tokenization Protecting Privacy | — |
| Matthew Honnibal | Explosion | Speaker | How many Labelled Examples Do You Need for a BERT-sized Model to Beat GPT4 on Predictive Tasks? | — |
| Michael Meredith | Capital One | Speaker | Low-latency Model Inference in Finance | — |
| Muller Mu | Roche | Speaker | Wanted: A Silver-Bullet Ml Ops Solution for Enterprise - Learnings from Implementing Sustainable Ml Ops in Pharma Research | — |
| Naiel Samaan | Ford Motor Company | Speaker | Tales of Innovation within a 100 Year Old Company | — |
| Niels Bantilan | Union.ai | Workshop (virtual) | Learn Your Codebase: Fine-tuning CodeLlama with Flyte… to Learn Flyte | — |
| Norm Zhou | Meta | Speaker | Beyond the Kaggle Paradigm: Future of End-to-End ML Platforms | — |
| Prakash Putt | Instacart | Speaker | Supercharging Search with LLMs: The Instacart Journey | — |
| Rajiv Shah | Hugging Face | Speaker | Evaluation Techniques for Large Language Models | — |
| Ramon Perez | Seldon | Workshop (virtual) | Introduction to Building ML Microservices: A Hands-On Approach with Examples from The Music Industry | — |
| Reuven Cohen | EY Americas | Speaker | Unlocking Potential: A Fireside Chat on Generative AI, Reasoning, and Logic in Enterprise Innovation | — |
| Richard Izzo | Lightning AI | Lightning | Supporting Community Competitions to Develop LLMs | — |
| Ryan McClelland | NASA | Speaker | Evolved Structures: Using AI and Robots to Build Spaceflight Structures at NASA | — |
| Ryan Turner | DVC | Lightning | How NOT to Get ML Models into Production | — |
| Salina Wu | Forethought | Workshop (virtual) | Avoid ML OOps with MLOps: A Modular Approach to Scaling Forethought’s E2 E Ml Platform | — |
| Sandeep Singh | Beans.AI | Speaker | Stable Diffusion for Your Images: Custom Dream | — |
| Serg Masis | Syngenta | Workshop (virtual) | QA in ML | — |
| Shaun Hillin | Cohere | Speaker | Behind the Hype....The Real Promise of LLM: Fundamentally Transforming Humanity’s Access to Innovation | — |
| Shreya Rajpal | Guardrails AI | Speaker | Your AI Applications Need Guardrails: Here's How To Build Them | — |
| Simba Khadder | Featureform | Workshop (virtual) | Feature Stores in Practice: Train and Deploy an End-to-End Fraud Detection Model with Featureform, Redis, and AWS | — |
| Simba Khadder | Featureform | Lightning | Feature Store are NOT about Storing Features | — |
| Sophia Yang | Anaconda | Speaker | Introduction to LangChain and Retrieval Augmented Generation (RAG) | — |
| Stefan Krawczyk | DAGWorks Inc | Speaker | Getting Higher Roi on Ml Ops Initiatives: Five Lessons Learned While Building out The Ml Ops Platform for 100+ Data Scientists | — |
| Susrutha Gongalla | Stitch Fix | Speaker | Supercharging Recommender Systems: Unleashing the Power of Distributed Model Training | — |
| Tibor Mach | DVC | Workshop (virtual) | Applying GitOps Principles at Every Step of An E2E MLOps Project - An Interactive Workshop | — |
| Valmir Bucaj | Ford Motor Company | Speaker | Tales of Innovation within a 100 Year Old Company | — |
| Ville Tuulos | Outerbounds | Workshop (virtual) | LLMs in Practice: A Guide to Recent Techniques and Trends | — |
| Vincent David | Capital One | Speaker | Low-latency Model Inference in Finance | — |
| William Falcon | Lightning AI | Speaker | Gen AI and Lightning: Accelerating AI Innovation while Ensuring Scalability and Security | — |
Sources
- https://web.archive.org/web/20230922002408/https://mlopsworld.com/
- https://web.archive.org/web/20231127150636/https://mlopsworld.com/speakers/
- https://web.archive.org/web/20230705171507/https://www.eventbrite.ca/e/4th-annual-mlops-world-conference-on-machine-learning-in-production-2023-tickets-615055847597