Conferences / Data Council
Data Council New York City 2019
Nov 12–13, 2019 · Columbia University, Alfred Lerner Hall, New York, NY, United States
- Conference
- Data Council Vendor & industry
- Dates
- Nov 12, 2019 – Nov 13, 2019
- Venue
- Columbia University, Alfred Lerner Hall
- Place
- New York, NY, United States
- Format
- In person
- Status
- Past
- Attendance
- —No reported headcount found; organizer aimed to keep attendance under 500.
- Website
- datacouncil.ai
- Recorded
- 18 sponsors · 38 speaker appearances
- Notes
- Sponsors are logos filtered to 'NYC '19' on the partners page; no tiers. 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 | — | web.archive.org | |
| Early Bird | $799 | — | web.archive.org | |
| Regular | $949 | — | web.archive.org | |
| Late Bird | $1,099 | — | web.archive.org |
Sponsors 18
Other 18
- Ascend.io · Partner
- Blackstone · Partner
- Capital One · Partner
- Cherre · Partner
- Citadel · Partner
- Crux · Partner
- Data Science Institute, Columbia University · Partner
- Datadog · Partner
- Dataform · Partner
- Guru · Partner
- IBM · Partner
- Microsoft · Partner
- Mode · Partner
- Qubole · Partner
- Reonomy · Partner
- Starburst · Partner
- The New York Times · Partner
- Watchful · Partner
Speakers and talks 38
| Speaker | Company | Role | Talk | Links |
|---|---|---|---|---|
| Hugo Bowne-Anderson | DataCamp | Keynote | Data Scientist, or the Most Dangerous Job of the 21st Century Strategy & culture | — |
| Nick Rockwell | The New York Times | Keynote | News in the Age of Algorithmic Recommendation Other | — |
| Riva-Melissa Tez | Intel | Keynote | Leveraging Compute Infrastructure & cost | — |
| Abe Gong | Great Expectations | Talk | Testing and Documenting Your Data <br> Doesn't Have to Suck Data quality & observability | — |
| Adi Polak | Treeverse | Talk | Building Efficient ML Pipelines and Responsible AI Solutions ML & MLOps | — |
| Alex Kass | DigitalOcean | Talk | The Observatorium - Using Machine Learning and Observability Together to Reduce Incident Impact ML & MLOps | — |
| Amelia White | Dstillery | Talk | Online Learning of Website Embeddings for Accurate Prediction of User Behavior Even When Data Are Scarce ML & MLOps | — |
| Barr Moses | Monte Carlo | Talk | 3 Best Practices for Data Organizations: Structure, ROI, Communications Strategy & culture | — |
| Benn Stancil | Mode | Talk | Building an End-to-End Data Stack in Thirty Minutes Analytics engineering | — |
| Daniel van der Ende | GoDataDriven | Talk | Reducing Flight Delays with Kubernetes and Tensorflow ML & MLOps | — |
| Frank McSherry | Materialize | Talk | The Materialize Incremental View Maintenance Engine Database internals | — |
| George Talbot | Talk | Monarch, Google’s Planet-Scale Streaming Monitoring Infrastructure Streaming | — | |
| Gideon Mann | Bloomberg | Talk | Augmented Programming GenAI & agents | — |
| Herman Schaaf | Skyscanner | Talk | From Batch to Streaming to Both: The Data Platform at Skyscanner Data engineering | — |
| Jerome Nilmeier | IBM | Talk | Uncovering the Potential of TensorFlow 2.0 ML & MLOps | — |
| Joe Ross | Splunk | Talk | Statistical Aspects of Distributed Tracing Data quality & observability | — |
| John Maiden | Cherre | Talk | Building a Knowledge Graph Using Messy Real Estate Data Operational databases | — |
| Joshua Loftus | New York University | Talk | Valid Inference after Model Selection and the selectiveInference Package Data science | — |
| Juliana Freire | New York University | Talk | Reproducibility in Data Science Data science | — |
| Kai Rikhye | Skillshare | Talk | Kubernetes-Native Workflow Orchestration with Argo Orchestration | — |
| Liwei Mao | Button | Talk | Building A Scalable Financial Data Store Operational databases | — |
| Lloyd Danzig | ICED(AI) | Talk | A Modern Love Story: Machine Learning & The Global Sports Betting Industry ML & MLOps | — |
| Lucy Vasserman | Jigsaw | Talk | Combating AI Bias at Scale ML & MLOps | — |
| Mark Etherington | Crux | Talk | Accelerate Source to Signal: Data Engineering Efficiency Data engineering | — |
| Maureen Teyssier | Reonomy | Talk | Creating Knowledge Graphs via a Symbiosis of Data Science and Data Engineering Operational databases | — |
| Mohammed Ridwanul | Dessa | Talk | Building Systems to Monitor Data and Model Health in Production Systems Data quality & observability | — |
| Nathan VanBenschoten | Cockroach Labs | Talk | CockroachDB: Architecture of a Geo-Distributed SQL Database Database internals | — |
| Ronny Luss | IBM | Talk | One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques ML & MLOps | — |
| Samantha Bail | — | Talk | Messy Data and Reluctant Users - The Trouble with Healthcare Data Data quality & observability | — |
| Sean Knapp | Ascend.io | Talk | Intelligent Orchestration - Data's Missing Link Orchestration | — |
| Seth Wiesman | Ververica | Talk | Leveraging Stateful Functions to Power the Next Generation of Event-Driven Applications Streaming | — |
| Shira Mitchell | Civis Analytics | Talk | Prediction-Based Decisions and Fairness: Choices, Assumptions, and Definitions Data science | — |
| Susana Marquez | Rockefeller Foundation | Talk | Intrinsic Autoregressive Models in Stan Data science | — |
| Tim van Cann | GoDataDriven | Talk | Reducing Flight Delays with Kubernetes and Tensorflow ML & MLOps | — |
| Vadim Semenov | Datadog | Talk | Tailor-S: Look What You Made Me Do Other | — |
| Viktor Gamov | Confluent | Talk | Real-time SQL Stream Processing at Scale with Apache Kafka and KSQL Streaming | — |
| William Falcon | Meta | Talk | A Label-Free World - Current State Of Unsupervised Deep Learning ML & MLOps | — |
| Yev Meyer | Guru | Talk | Empowering Customer-Facing Teams With Voice-Based AI GenAI & agents | — |