Conferences / MLOps World (Machine Learning in Production)
3rd Annual MLOps World 2022
Jun 9–10, 2022 · Sheraton Centre Toronto Hotel, Toronto, ON, Canada
- Conference
- MLOps World (Machine Learning in Production) Data science & ML
- Dates
- Jun 9, 2022 – Jun 10, 2022
- Venue
- Sheraton Centre Toronto Hotel
- Place
- Toronto, ON, Canada
- Format
- In person
- Status
- Past
- Attendance
- —
- Website
- mlopsworld.com
- Recorded
- 33 sponsors · 131 speaker appearances
- Notes
- In-person conference & expo June 9-10, 2022 (Sheraton Centre, 123 Queen St W); bonus virtual workshops for ticket holders June 7-8. Talks not streamed. Speakers: full list from the 2022 speakers page (incl. Speaker's Corner/tradeshow talks); one entry per speaker-talk pair. Sponsor names derived from logo links (no alt text). Eventbrite showed ticket range $0 - $330.20 (currency not shown; tier names not captured), so no prices recorded.
Ticket prices
No ticket prices recorded
Sponsors 33
Platinum 1
- Aporia · Platinum Sponsor
Gold 9
Silver 11
- Agnostiq (Covalent) · Silver Sponsors
- Arize AI · Silver Sponsors
- Featureform · Silver Sponsors
- Genesis · Silver Sponsors
- MosaicML · Silver Sponsors
- Ocado Technology · Silver Sponsors
- Petuum · Silver Sponsors
- Redis · Silver Sponsors
- Robust Intelligence · Silver Sponsors
- Tecton · Silver Sponsors
- Tenstorrent · Silver Sponsors
Bronze 7
- Harnham · Bronze Sponsors
- PICSELLIA · Bronze Sponsors
- Private AI · Bronze Sponsors
- SharpestMinds · Bronze Sponsors
- TrojAI · Bronze Sponsors
- UbiOps · Bronze Sponsors
- VESSL AI · Bronze Sponsors
Community 4
- Aggregate Intellect · Community Partners
- MLOps Community · Community Partners
- Vector Institute · Community Partners
- Women in AI & Robotics · Community Partners
Other 1
- Amazon Web Services · Start-Up Showcase, Demo & Career Fair Sponsor
Speakers and talks 131
| Speaker | Company | Role | Talk | Links |
|---|---|---|---|---|
| Achal Shah | Tecton | Workshop | Building Real-Time ML Features with Feast, Spark, Redis, and Kafka | — |
| Afsoon Soudi | CBC/RADIO-CANADA | Speaker | AI in Robotics, Manufacturing, and Media: How Good Practices Can Shape the Future | — |
| Alon Gubkin | Aporia | Speaker | Monitoring ML Models in Production with Aporia | — |
| Alon Gubkin | Aporia | Speaker | Building Production ML Monitoring from Scratch: Live Coding Session | — |
| Amale El Hamri | Artefact | Speaker | Handle MLOps Challenges on a Real World Use Case at Decathlon | — |
| Aman Khan | Arize AI | Speaker | Becoming An ML Platform Power Builder Powered by ML Observability | — |
| Amber Roberts | Arize AI | Speaker | The Key Pillars of ML Observability and How to Apply Them to Your ML Systems | — |
| Amrit Krishnan | Vector Institute | Speaker | CyclOps – A Framework for Data Extraction, Model Evaluation and Drift Detection for Clinical Use-Cases | — |
| Amy Bachir | Interos | Speaker | A GitOps Approach to Machine Learning | — |
| Andrea Olgiati | Amazon Web Services | Speaker | Panel: Embedding Diversity and Fairness Into Your Model Governance | — |
| Anindya Datta | Mobilewalla | Speaker | Feature Engineering Made Simple | — |
| Anneya Golob | Shopify | Speaker | Panel: What Every Product Manager Delivering AI Solutions Should Know | — |
| Anouk Dutrée | UbiOps | Workshop | A Zero-Downtime Set-up for Models: How and Why | — |
| Archit Kulkarni | Anyscale | Workshop | Introduction to Model Deployment with Ray Serve | — |
| Arthur Berrill | Royal Bank of Canada | Speaker | Panel: Embedding Diversity and Fairness Into Your Model Governance | — |
| Asim Sultan | Rocket Science Development | Speaker | Hands-on : A Beginner-Friendly Crashcourse to Kubernetes | — |
| Asmita Usturge | Microsoft | Speaker | What Do Engineers Not Get About Working with Data Scientists? | — |
| Aurick Qiao Ph.D | Petuum | Workshop | Supercharging MLOps with the Petuum Platform | — |
| Azin Asgarian | Georgian | Speaker | What Do Engineers Not Get About Working with Data Scientists? | — |
| Bernease Herman | WhyLabs | Speaker | It’s All About The Data: Continuously Improve ML Models, The Data-Centric Way | — |
| Bhaktipriya Radharapu | Speaker | MLOps for Fairness – Creating Comprehensive Fairness Workflows | — | |
| Bobby Linsey | Amazon Web Services | Workshop | Implementing MLOps Practices on AWS using Amazon SageMaker | — |
| Charles Huang | Speaker | Managing Human in the Loop Systems Without Burning Out Your Engineers | — | |
| Cheng Chen | Loblaw Digital | Speaker | Supporting Sales Forecasting at Scale for Canada’s Largest Grocery Store | — |
| Clayton Davis | Modzy | Workshop | What’s in the Box? Automatic ML Model Containerization | — |
| Corentin Vasseur | Decathlon | Speaker | Handle MLOps Challenges on a Real World Use Case at Decathlon | — |
| Courtney Branson | IBM | Workshop | MLOps for Trustworthy AI | — |
| Daniel Butnaru | Roche | Workshop | Generalizing Diversity: Machine Learning Operationalization for Pharma Research | — |
| Daniel Fleck | IBM | Workshop | MLOps for Trustworthy AI | — |
| Daniel Glogowski | Robust Intelligence | Workshop | Using RIME to Eliminate AI Failures | — |
| Danny Chiao | Tecton | Workshop | Building Real-Time ML Features with Feast, Spark, Redis, and Kafka | — |
| Davor Capalija | Tenstorrent | Workshop | Scaling Out Production Workloads on Next-Gen AI Accelerators | — |
| Dean Pleban | DagsHub | Speaker | Solving MLOps From First Principles: A Framework to Reduce Complexity | — |
| Deepika Srinivasan | eBay | Workshop | Scaling ML Embedding Models to Serve a Billion Queries | — |
| Demetrios Brinkmann | MLOps Community | Speaker | What Do Engineers Not Get About Working with Data Scientists? | — |
| Demetrios Brinkmann | MLOps Community | Speaker | What Engineers Need To Know About Building for Data scientists | — |
| Diego Klabjan | Northwestern University | Speaker | MLOps for Deep Learning | — |
| Dmitry Petrov | Iterative | Speaker | Model Registry with open source tools: Git & GitHub | — |
| Dr. Chi Wang | Microsoft | Workshop | Automated Machine Learning & Tuning with FLAML | — |
| Dr. Qingyun Wu | Penn State University | Workshop | Automated Machine Learning & Tuning with FLAML | — |
| Dvir Dukhan | Redis | Speaker | Delivering Fast Machine Learning with Redis Enterprise Cloud | — |
| Ehsan Amjadian | RBC | Speaker | Cutting-Edge NLP, Large Language Models, and Their Implications For Products and Research | — |
| Eric Hammel | Rocket Science Development | Speaker | Hands-on : A Beginner-Friendly Crashcourse to Kubernetes | — |
| Gandalf Hernandez | Spotify | Speaker | Becoming An ML Platform Power Builder Powered by ML Observability | — |
| Gerard Desmond | Tenstorrent | Speaker | AI Silicon & ML Systems | — |
| Hagay Lupesko | MosaicML | Speaker | Understanding Foundation Models: A New Paradigm for Building and Productizing AI Systems | — |
| Harpreet Sahota | Pachyderm | Speaker | Taking MLOps 0-60: How to Version Control, Unify Data and Manage Code Lifecycles | — |
| Hector Liu Ph.D | Petuum | Workshop | Supercharging MLOps with the Petuum Platform | — |
| Ignacio Amaya de la Peña | Rovio | Speaker | MLOps at Rovio For Personalization (Self-Service Reinforcement Learning in Production) | — |
| Isaac Vidas | Shopify | Speaker | Shopify’s ML Platform Journey Using Open Source Tools. Case Study Building Merlin & AMA | — |
| Itay Ben Haim | Superwise | Workshop | A Guide to Building a Continuous MLOps Stack | — |
| Jack Jin | Zoom | Workshop | Scale and Accelerate the Distributed Model Training in Kubernetes Cluster | — |
| Jacob Tomlinson | NVIDIA | Workshop | Parallelizing your ETL with Dask on Kubeflow | — |
| Jacopo Tagliabue | Coveo | Speaker | Wild Wild Tests: Testing Recommender Systems in the Wild | — |
| Jaeman An | VESSL AI | Speaker | How We Reduced 83% of ML Computing Cost on 100+ ML Projects | — |
| James Stewart | TrojAI | Speaker | Robustness and Security for AI and the Dangerous Dismissal of Edge Cases | — |
| Jerry Liu | Robust Intelligence | Workshop | Using RIME to Eliminate AI Failures | — |
| Jessie Lamontagne | Kinaxis | Speaker | Managing A Data Science Team During the Great Resignation / Panel: What Every Product Manager Delivering AI Solutions Should Know | — |
| Jim Dowling | Hopsworks | Workshop | Personalized Recommendations and Search with Retrieval and Ranking at scale on Hopsworks | — |
| Jimmy Whitaker | Pachyderm | Workshop | Taking MLOps 0-60: How to Version Control, Unify Data and Manage Code Lifecycles | — |
| Jonathan Messiha | IBM | Workshop | MLOps for Trustworthy AI | — |
| Josh Goldstein | Seldon | Speaker | Deploying Machine Learning Models at Scale Using Seldon | — |
| Josh Goldstein | Seldon | Workshop | Deploying and Managing Machine Learning Models at Scale: A Hands-on Workshop with Seldon | — |
| Josh Sullivan | Modzy | Speaker | Untold Secrets for Successful MLOps at Scale | — |
| Jules Damji | Anyscale | Workshop | Introduction to Model Deployment with Ray Serve | — |
| Karime Pereida | Ocado Technology | Speaker | RL from Simulation to Production | — |
| Kathryn Hume | Royal Bank of Canada | Speaker | Panel: Embedding Diversity and Fairness Into Your Model Governance | — |
| Kevin Kho | Prefect | Workshop | Lessons Learned from DAG-based Workflow Orchestration | — |
| Kirit Thadaka | Amazon Web Services | Workshop | Implementing MLOps Practices on AWS using Amazon SageMaker | — |
| Kory Fong | Private AI | Speaker | Privacy Risks in NLP & Ways to Combat Them | — |
| Lewis Tunstall | Hugging Face | Workshop | Accelerating Transformers with Hugging Face Optimum and Infinity | — |
| Lina Weichbrodt | DKB | Workshop | Machine Learning Monitoring in Production: Lessons Learned from 30+ Use Cases | — |
| Lior Gavish | Monte Carlo | Workshop | How to Treat Your Data Platform Like a Product: 5 Key Best Practices | — |
| Maia Brenner | Tryolabs | Speaker | Production System and Not Go Up in Smoke | — |
| Manal Siddiqui | Transitional Forms | Speaker | EntertAInment Evolved: Co-Creating Stories with AI | — |
| Maryam Emami | AI Materia | Speaker | AI in Robotics, Manufacturing, and Media: How Good Practices Can Shape the Future | — |
| Mefta Sadat | Loblaw Digital | Speaker | Supporting Sales Forecasting at Scale for Canada’s Largest Grocery Store | — |
| Miguel Gonzalez-Fierro | Microsoft | Workshop | Implementing a Parallel MLOps Test Pipeline for Open Source Development | — |
| Mihir Mathur | Lyft | Workshop | Defending Against Decision Degradation with Full-Spectrum Model Monitoring : Case Study and AMA | — |
| Mike Del Balso | Tecton | Speaker | Building Real-Time ML Features with a Feature Platform | — |
| Mikhail Sveshnikov | Iterative | Workshop | How to MLEM your Models to Production | — |
| Milecia McGregor | Iterative | Speaker | How to Conquer Data Drift & Prevent Stale Models in Production using DVC | — |
| Mohamed Sabri | Rocket Science Development | Speaker | Hands-on : A Beginner-Friendly Crashcourse to Kubernetes | — |
| Moses Guttmann | ClearML | Speaker | MLOps without the Buzzwords. So What’s Left? | — |
| Nahla Salem | Yelp | Speaker | Panel: What Every Product Manager Delivering AI Solutions Should Know | — |
| Niels Bantilan | Union.ai | Speaker | UnionML: A Microframework for Building Machine Learning Applications | — |
| Niklas Nielsen | MosaicML | Speaker | Efficient ML Training | — |
| Nima Mahmoudi | TELUS | Speaker | SLA-Aware Machine Learning Inference Serving on Serverless Computing Platforms | — |
| Niv Hertz | Aporia | Workshop | ML Monitoring on Edge Devices | — |
| Olivier Blais | Moov AI | Speaker | It’s Time to Start Delivering High-Quality ML Models | — |
| Or Itzary | Superwise | Speaker | A Framework for A Successful Continuous Training Strategy | — |
| Oren Razon | Superwise | Speaker | Lessons Learned from ML Monitoring Failures | — |
| Patricia Thaine | Private AI | Speaker | How MLOps Tools Will Need to Adapt to Responsible and Ethical AI: Stay Ahead of the Curve | — |
| Paul Wu | Private AI | Speaker | Privacy Risks in NLP & Ways to Combat Them | — |
| Pavel Klushin | Qwak | Workshop | Deploy High Scale ML Models Without the Hustle | — |
| Philipp Schmid | Hugging Face | Workshop | Accelerating Transformers with Hugging Face Optimum and Infinity | — |
| Phillip Gornicki | Kinaxis | Speaker | Panel: What Every Product Manager Delivering AI Solutions Should Know | — |
| Quoc-Tien Au | Manifest Climate | Speaker | Evolution of MLOps at an Early-Stage Startup | — |
| Richard Mah | MosaicML | Speaker | Efficient ML Training | — |
| Robert Crowe | Speaker | Production ML for Mission-Critical Applications | — | |
| Ryan Irwin | Yelp | Speaker | Low-latency Neural Network Inference for ML Ranking Applications | — |
| Santosh Radha PhD | Agnostiq (Covalent) | Speaker | Covalent: A Workflow Manager for Hardware and Language Agnostic Distributed ML Workflows | — |
| Sara Sun | Scotiabank | Speaker | What Do Engineers Not Get About Working with Data Scientists? | — |
| Saumil Dave | Modzy | Workshop | What’s in the Box? Automatic ML Model Containerization | — |
| Sedef Akinli Kocak | Vector Institute | Speaker | What Do Engineers Not Get About Working with Data Scientists? | — |
| Senthilkumar Gopal | eBay | Workshop | Scaling ML Embedding Models to Serve a Billion Queries | — |
| Shabnam Mokhtarani | Featureform | Speaker | The Future of Feature Stores | — |
| Shaikh Quader | IBM | Speaker | In-Database Machine Learning with IBM Db2 | — |
| Shelbee Eigenbrode | Amazon Web Services | Speaker | Top 5 Lessons Learned in Helping Organizations Adopt MLOps Practices / Implementing MLOps Practices on AWS using Amazon SageMaker | — |
| Shimona Narang | Scotiabank | Speaker | Scotiabank’s Path Towards Accelerated Analytics Through GCP | — |
| Shivam Bharuka | Meta | Speaker | Machine Learning Infrastructure at Meta Scale | — |
| Simba Khadder | Featureform | Speaker | The Future of Feature Stores | — |
| Simba Khadder | Featureform | Speaker | An Introduction to Featureform, the Virtual Feature Store | — |
| Stephan Brown | Interos | Speaker | A GitOps Approach to Machine Learning | — |
| Stephan Zheng | Salesforce | Workshop | WarpDrive: Orders-of-Magnitude Faster Multi-Agent Deep RL on a GPU | — |
| Suraj Subramanian | PyTorch | Speaker | Leaner, Greener and Faster Pytorch Inference with Quantization | — |
| Tomi Poutanen | Signal 1 AI | Speaker | Transformative Power of ML in the Real World | — |
| Tong Wen Ph.D | Petuum | Workshop | Supercharging MLOps with the Petuum Platform | — |
| Vallijah Subasri | Vector Institute | Speaker | CyclOps – A Framework for Data Extraction, Model Evaluation and Drift Detection for Clinical Use-Cases | — |
| Varun Chugh | Mobilewalla | Speaker | Feature Engineering Made Simple | — |
| Victor Pereboom | UbiOps | Speaker | Deploying & Orchestrating AI/ML Models with UbiOps | — |
| Victor Sonck | ClearML | Workshop | MLOps is Just HPC in Disguise. A Real-World, No Nonsense Guide to Upgrading Your Workflow | — |
| Victor Yap | Rev.com | Speaker | One Cluster to Rule Them All – ML on the Cloud Using Ray on Kubernetes and AWS | — |
| Vinnie Saini | Scotiabank | Workshop | Critical Use of MLOps in Finance: Using Cloud-managed ML Services that Scale | — |
| Yaron Haviv | Iguazio | Workshop | MLOps Beyond Training: Simplifying and Automating the Operational Pipeline | — |
| Yaron Singer | Robust Intelligence | Speaker | Eliminating AI Risk, One Model Failure at A Time | — |
| Yegna Subramanian Jambunath | Northwestern University | Speaker | MLOps for Deep Learning | — |
| Yue Hu | University of Waterloo | Speaker | AI in Robotics, Manufacturing, and Media: How Good Practices Can Shape the Future | — |
| Yuval Fernbach | Qwak | Speaker | Crossing the Production ML Chasm: The 5 Biggest Challenges in Model Productionization and Practical Tools to Address them | — |
| Yuval Fernbach | Qwak | Speaker | The Critical Things you HAVE to Build to Transform Your Company to be ML Driven | — |
| Yuvrender Gill | BenchSci | Speaker | The Future of Feature Stores | — |
Sources
- https://web.archive.org/web/20220528220403/https://mlopsworld.com/
- https://web.archive.org/web/20221205110251/https://mlopsworld.com/2022-speakers/
- https://web.archive.org/web/20220528195230/https://www.eventbrite.ca/e/3rd-annual-mlops-world-conference-on-machine-learning-in-production-2022-tickets-250122682677