Conferences / Data Council
Data Council San Francisco 2019
Apr 17–18, 2019 · Holiday Inn Golden Gateway, San Francisco, CA, United States
- Conference
- Data Council Vendor & industry
- Dates
- Apr 17, 2019 – Apr 18, 2019
- Venue
- Holiday Inn Golden Gateway
- Place
- San Francisco, CA, United States
- Format
- In person
- Status
- Past
- Attendance
- —No reported headcount found; organizer said it limited attendance to 500 per event.
- Website
- datacouncil.ai
- Recorded
- 11 sponsors · 47 speaker appearances
- Notes
- Sponsors are the logos filtered to 'SF '19' on the organizer's partners page; no tiers were published. 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 |
|---|---|---|---|---|
| Early Bird | $799 | Feb 10, 2019 | web.archive.org | |
| Regular | $949 | — | web.archive.org | |
| Last Minute | $1,099 | — | web.archive.org |
Sponsors 11
Other 11
- Astronomer · Partner
- CARTO · Partner
- Circonus · Partner
- Crux · Partner
- Datacoral · Partner
- Elastic · Partner
- Heroku · Partner
- IBM · Partner
- Mode · Partner
- Salesforce · Partner
- Stitch Fix · Partner
Speakers and talks 47
| Speaker | Company | Role | Talk | Links |
|---|---|---|---|---|
| Catherine Tarsney | Democratic National Committee | Keynote | How Data is Transforming Politics Other | — |
| Abe Gong | Great Expectations | Talk | Data Security and Privacy in the Age of Machine Learning Governance & security | — |
| Alex Ratner | Stanford University | Talk | Accelerating Machine Learning with Training Data Management ML & MLOps | — |
| Ali Hamidi | Salesforce | Talk | Operating Multi-Tenant Kafka Services for Developers on Heroku Streaming | — |
| Amit Ramesh | Yelp | Talk | End-to-end Exactly-once Aggregation Over Ad Streams Streaming | — |
| Andrew Colombi | Tonic | Talk | Making Friends with Generative Models GenAI & agents | — |
| Andrew Hoh | Airbnb | Talk | Bighead: Airbnb's end-to-end Machine Learning Platform ML & MLOps | — |
| Andy Eschbacher | CARTO | Talk | Spatial Data Science Methods for Improving Models Data science | — |
| Anish Doshi | Trifacta | Talk | Building a Programming By Example (PBE) Framework in Trifacta: Lessons learned and implications for Data Analytics Analytics engineering | — |
| Anitha Kannan | Curai | Talk | Scaling the best healthcare to everyone, with AI Other | — |
| Arun Krishnaswamy | Workday | Talk | Data Security and Privacy in the Age of Machine Learning Governance & security | — |
| Austen Head | Quid | Talk | Appifying Data Science Workflows to Create Composable, User-Friendly Data Pipeline Products Data engineering | — |
| Austin Wilt | Slack | Talk | Balancing Broad Data Access with Usability at Scale Governance & security | — |
| Barr Moses | Monte Carlo | Talk | Introducing Data Downtime: From Firefighting to Winning Data quality & observability | — |
| Bin Fan | Alluxio | Talk | Building a Distributed Data Access Layer for Analytics on Any Cloud Data engineering | — |
| Carl Gold | Zuora | Talk | Fighting Churn with Data Data science | — |
| Dan Robinson | Heap | Talk | When Testing in Production is a Good Idea Data quality & observability | — |
| Durgam Vahia | Talk | Building a Data-Powered Sales Intelligence Platform Strategy & culture | — | |
| Edwin Ong | TinyData | Talk | Scaling Data Products Under Startup Constraints: A Case Study of ML Bias Testing ML & MLOps | — |
| Ethan Guo Yihua | Uber | Talk | Powering Uber's global network analytics pipelines in near real-time with Apache Hudi (Incubating) Delta Streamer Warehouse & lakehouse | — |
| Gian Merlino | Imply | Talk | Swimming in the Data River, or, when “Streaming Analytics” isn’t Streaming | — |
| Greg Neiheisel | Astronomer | Talk | Running Airflow reliably with Kubernetes Orchestration | — |
| Hedi Razavi | Keewi | Talk | Building Resilient Machine Learning Pipelines with IoT Data ML & MLOps | — |
| Igor Canadi | Rockset | Talk | Architecting a Low-Latency Schemaless SQL Engine Database internals | — |
| Ion Stoica | RISE | Talk | Ray for Reinforcement Learning ML & MLOps | — |
| Jerome Nilmeier | IBM | Talk | Time Series Prediction with TensorFlow ML & MLOps | — |
| Johannes Leppä | Komodo Health | Talk | Scalable Data Ingestion Architecture Using Airflow and Spark Data engineering | — |
| Josh Ferguson | Mode | Talk | Introducing Switch: A Framework for Custom Data Applications BI & semantic layer | — |
| Julian Hyde | Talk | Tactical Data Engineering Data engineering | — | |
| Kai Brusch | Airbnb | Talk | Delphi: A Hybrid Approach to Forecasting a Global Marketplace Data science | — |
| Karthik Ranganathan | Yugabyte | Talk | Distributed SQL Databases Deconstructed Database internals | — |
| Kelley Rivoire | Stripe | Talk | Scaling model training: from flexible training APIs to resource management with Kubernetes ML & MLOps | — |
| Kishore Gopalakrishna | Founding Engineer | Talk | Building Real-Time Analytics Applications Using Apache Pinot: A Case Study of LinkedIn Streaming | — |
| Luke Merrick | Fiddler AI | Talk | Explaining AI: Putting Theory into Practice ML & MLOps | — |
| Matt Seal | Netflix | Talk | Notebooks as Functions with Papermill Data science | — |
| Max Beauchemin | Preset | Talk | The history and anatomy of Apache Superset BI & semantic layer | — |
| Nick Schrock | Dagster Labs | Talk | Dagster: A New Programming Model for Data Processing Orchestration | — |
| Nishith Agarwal | Uber | Talk | Powering Uber's global network analytics pipelines in near real-time with Apache Hudi (Incubating) Delta Streamer Warehouse & lakehouse | — |
| Paul Cothenet | MadKudu | Talk | Building a Lean AI Startup - Lessons Learned Strategy & culture | — |
| Praneeth Vepakomma | Massachusetts Institute of Technology | Talk | Split Learning: A Resource Efficient Distributed Deep Learning Method without Sensitive Data Sharing ML & MLOps | — |
| Praneeth Vepakomma | Massachusetts Institute of Technology | Talk | Data Security and Privacy in the Age of Machine Learning Governance & security | — |
| Rupa Parameswaran | Demandbase | Talk | Data Security and Privacy in the Age of Machine Learning Governance & security | — |
| Ryan Smith | Wootric | Talk | Transfer Learning in NLP - How to Help Small Teams Account for Small Datasets ML & MLOps | — |
| Sid Choudhury | Yugabyte | Talk | Distributed SQL Databases Deconstructed Database internals | — |
| Soups Ranjan | Revolut | Talk | Data Security and Privacy in the Age of Machine Learning Governance & security | — |
| Spencer Barton | Branch | Talk | Machine Learning Infrastructure at an Early Stage ML & MLOps | — |
| Tao Feng | Lyft | Talk | Amundsen: A Data Discovery Platform From Lyft Governance & security | — |
Sources
- https://web.archive.org/web/20190217120419/https://www.datacouncil.ai/san-francisco
- https://web.archive.org/web/20190403024855/https://www.datacouncil.ai/san-francisco
- https://web.archive.org/web/20190819035555/https://www.datacouncil.ai/partners
- https://www.aicouncil.com/past-events/2019-sf
- https://www.aicouncil.com/past-events