Conferences / Data Council
Data Council Austin 2022
Mar 23–24, 2022 · AT&T Hotel and Conference Center, Austin, TX, United States
- Conference
- Data Council Vendor & industry
- Dates
- Mar 23, 2022 – Mar 24, 2022
- Venue
- AT&T Hotel and Conference Center
- Place
- Austin, TX, United States
- Format
- In person
- Status
- Past
- Attendance
- —
- Website
- datacouncil.ai
- Recorded
- 34 sponsors · 64 speaker appearances
- Notes
- Only 2022 in-person event; originally scheduled for Jan 27-28, 2022 and rescheduled to March. Venue from organizer's Meetup listing. Some sponsor logos had unclear alt text (e.g. 'y2', 'Gest', 'Semitext', 'Rasoo', 'LI'); names are taken from the logo link targets. The OpenTeams logo linked to selectstar.com on the page, so that name is from alt text only. Ticket tiers on the page appear identical to the 2019 NYC template and may have been carried over. Speakers: all talks listed in the AI Council (formerly Data Council) past-events archive; affiliations as given there, which may reflect a speaker's title at a later date.
Ticket prices
| Ticket | Price | Valid until | Notes | Source |
|---|---|---|---|---|
| Super Early Bird | $699 | — | All shown as sold out on the post-event snapshot | web.archive.org |
| Early Bird | $799 | — | All shown as sold out on the post-event snapshot | web.archive.org |
| Regular | $949 | — | All shown as sold out on the post-event snapshot | web.archive.org |
| Late Bird | $1,099 | — | All shown as sold out on the post-event snapshot | web.archive.org |
Sponsors 34
Platinum 1
Bronze 19
Community 4
- Anaconda · Community Partners
- Data Engineering Weekly · Community Partners
- NumFOCUS · Community Partners
- RTInsights · Community Partners
Speakers and talks 64
| Speaker | Company | Role | Talk | Links |
|---|---|---|---|---|
| Diego Oppenheimer | DataRobot | Keynote | DevOps for Machine Learning and other half-Truths Processes and tools for the ML lifecycle ML & MLOps | — |
| Abe Gong | Great Expectations | Talk | How to Create a Shared, Open Standard for Data Quality Data quality & observability | — |
| Adi Polak | Treeverse | Talk | Get Ready for ML! Level Up Your Data Lake with Delta and lakeFS Warehouse & lakehouse | — |
| Ahmed Elsamadisi | Narrator | Talk | How to Finally Get Self-Service ... By Ditching the Star Schema BI & semantic layer | — |
| Alvaro Soto | Procore Technologies | Talk | Data and AI in Construction Other | — |
| Anand Ranganathan | Unscrambl | Talk | Exploring Data Through Natural-Language & Conversational Analytics GenAI & agents | — |
| Anirudh Shah | Iggy | Talk | Iggy: Build better models with location data ML & MLOps | — |
| Anupam Datta | TruEra | Talk | AI Monitoring and Explainability - the Critical, Hidden Connection ML & MLOps | — |
| Bastien Boutonnet | Soda | Talk | Don't Let Your ML Models Decay! ML & MLOps | — |
| Benn Stancil | Mode | Talk | The Return of the OLAP Cube BI & semantic layer | — |
| Bernease Herman | WhyLabs | Talk | Observability at the long tail: why sampling production data doesn’t work for rare events Data quality & observability | — |
| Bogomil Balkansky | Sequoia Capital | Talk | The Next Big Opportunities in Data Infrastructure Other | — |
| Caitlin Colgrove | Hex | Talk | Beyond Linear Notebooks: Implementing Reactivity with IPython Data science | — |
| Chad Sanderson | Gable.ai | Talk | Getting ROI from Experimentation: How AB Experimentation plays out in Organizations Data science | — |
| Chang She | LanceDB | Talk | Rikai: a new data format for analytics on unstructured data at scale Warehouse & lakehouse | — |
| Chaoyu Yang | BentoML | Talk | High-performance Model Serving in Python, using BentoML ML & MLOps | — |
| Clare Gollnick | Fathom | Talk | Designing for life or death: building data products for biopharma operations with Fathom Strategy & culture | — |
| Clodéric Mars | Air | Talk | Towards Human-AI Teaming: Intelligence Ecosystems to Tackle High-Stakes Use Cases GenAI & agents | — |
| Danny Chiao | Tecton | Talk | Building High Performance Recommender Systems with Feature Stores ML & MLOps | — |
| Devin Petersohn | Ponder | Talk | Scaling up your pandas workflows with Modin Data science | — |
| Egor Gryaznov | Bigeye | Talk | Data Reliability Engineering: A New Approach to Data Quality Data quality & observability | — |
| Elizabeth Dlha | Deepnote | Talk | Notebooks Got Your Modern Data Back Data science | — |
| Emily Curtin | Intuit | Talk | Making Humans and Code GPU-Capable at Mailchimp Strategy & culture | — |
| George Mathew | Insight | Talk | The Next Big Opportunities in Data Infrastructure Other | — |
| Hung Dang | Y42 | Talk | Using GIT as a NoSQL Database for Fine-Grained Control Over the Data Pipeline Data engineering | — |
| James Campbell | Superconductive | Talk | What is a Dataset? Emerging Core Concepts in the Modern Data Stack Governance & security | — |
| Jesika Haria | LogicLoop | Talk | How 200+ Leaders Made Business Data Work Harder Strategy & culture | — |
| Jessica Laughlin | Materialize | Talk | Materialize+dbt: Streaming for the Modern Data Stack Streaming | — |
| Joanne Chen | Foundation | Talk | The Next Big Opportunities in Data Infrastructure Other | — |
| Jules Damji | Anyscale | Talk | Ray: A Framework for Scaling and Distributing Python & ML Applications ML & MLOps | — |
| Julie Hollek | Mozilla | Talk | How to Teach an Old Dog New Tricks: Modernizing a Business with Data Products Strategy & culture | — |
| Julien Le Dem | Datadog | Talk | Data Lineage with Apache Airflow using OpenLineage Data quality & observability | — |
| Kashish Gupta | Hightouch | Talk | Data Apps: Data warehouse as a platform Warehouse & lakehouse | — |
| Kyle Eaton | Great Expectations | Talk | How to Create a Shared, Open Standard for Data Quality Data quality & observability | — |
| Maggie Hays | Acryl Data | Talk | The Data Practitioners Guide to Data Discovery Governance & security | — |
| Max Beauchemin | Preset | Talk | The Future of Business Intelligence BI & semantic layer | — |
| Mingsheng Hong | Bluesky Data | Talk | The Sky's the Limit -- RE: The Next 10 Years of Data Infra on Clouds Infrastructure & cost | — |
| Nick Schrock | Dagster Labs | Talk | What is a Dataset? Emerging Core Concepts in the Modern Data Stack Governance & security | — |
| Niels Bantilan | Union.ai | Talk | Type-Safe Data Processing and Machine Learning Pipelines with Flyte and Pandera ML & MLOps | — |
| Oana Olteanu | SignalFire | Talk | The Next Big Opportunities in Data Infrastructure Other | — |
| Paul Singman | Treeverse | Talk | Get Ready for ML! Level Up Your Data Lake with Delta and lakeFS Warehouse & lakehouse | — |
| Paula Griffin | Zip Recruiter | Talk | The Life-Changing Magic of Data Governance Governance & security | — |
| Pete Soderling | AI Council & Zero Prime Ventures | Talk | The Next Big Opportunities in Data Infrastructure Other | — |
| Peter Gao | Aquarium | Talk | It's The Data, Stupid! Other | — |
| Peter Wang | Anaconda | Talk | Enterprise Data Science Comes of Age Data science | — |
| Pim Nauts | STRM Privacy | Talk | Data in, privacy out: data streams for the privacy age with STRM Privacy Governance & security | — |
| Prukalpa Sankar | Atlan | Talk | What is a Dataset? Emerging Core Concepts in the Modern Data Stack Governance & security | — |
| Rick Lamers | Orchest | Talk | A Quick Tour of the Orchest OSS project Orchestration | — |
| Robert Yi | Hyperquery | Talk | Dashboards, dashboards everywhere: how to fix analytics collaboration BI & semantic layer | — |
| Ryan Blue | Databricks | Talk | Why You Shouldn’t Care About Iceberg Warehouse & lakehouse | — |
| Sandy Ryza | Elementl | Talk | Rethinking Orchestration as Reconciliation: Software-Defined Assets in Dagster Orchestration | — |
| Shayan Mohanty | Watchful | Talk | Arguments Against Hand Labeling ML & MLOps | — |
| Shinji Kim | Select Star | Talk | Data Discovery: Getting More From Your Metadata Governance & security | — |
| Shirshanka Das | Acryl Data | Talk | The Data Practitioners Guide to Data Discovery Governance & security | — |
| Shirshanka Das | Acryl Data | Talk | What is a Dataset? Emerging Core Concepts in the Modern Data Stack Governance & security | — |
| Steven Hillion | Astronomer | Talk | What is a Dataset? Emerging Core Concepts in the Modern Data Stack Governance & security | — |
| Susannah Shattuck | Credo AI | Talk | Building Responsible AI: Best Practices Across the Product Development Lifecycle Governance & security | — |
| Tristan Zajonc | Continual | Talk | The Case for Declarative Machine Learning ML & MLOps | — |
| Ville Tuulos | Outerbounds | Talk | The Modern Stack for ML Infrastructure ML & MLOps | — |
| Vinoth Chandar | Onehouse | Talk | Lakehouse or Warehouse - Where Should You Live? Warehouse & lakehouse | — |
| Vishakha Gupta-Cledat | ApertureData | Talk | Just Because Your Data Is Unstructured Doesn’t Mean it Should be Onerous Data engineering | — |
| Will Thompson | Privacy Dynamics | Talk | Privacy Plus Utility: Preserving Data Insights with State-of-the-Art Privacy Protection Governance & security | — |
| Willy Lulciuc | Datakin | Talk | Data Lineage with Apache Airflow using OpenLineage Data quality & observability | — |
| Yaroslav Tkachenko | Shopify | Talk | Apache Flink Adoption at Shopify Streaming | — |
Sources
- https://web.archive.org/web/20220331180450/https://www.datacouncil.ai/austin
- https://web.archive.org/web/20211109193252/https://www.datacouncil.ai/austin
- https://www.meetup.com/datacouncil-ai-london-data-engineering-and-science/events/284053160
- https://www.rtinsights.com/data-council-austin-a-melting-pot-for-everyone-working-with-data/
- https://www.aicouncil.com/past-events/2022