The team at Spotify talk about the challenges when building a central feature infrastructure at a highly autonomous organization.
The Hopsworks team presents how to leverage the Feature Store to make real-time predictions, with low-latency and at scale.
The team at Salesforce discusses how the company leverages from the Feature Store to build a collaborative environment across teams.
Yaron Haviv from Iguazio talks how the feature store supports real-time and batch use cases across training and serving environments.
David Liu talks about how Twitter solved challenges of collaboration and shareability with a centralized feature store.
Moderated by Mike Klaczynski from Snowflake.
Spotify, Salesforce, Twitter, Hopsworks, Iguazio and Disney discuss the significant role of feature stores as a company scales.
Mark Roy from Amazon talks how SageMaker can help to accelerate the ML lifecycle, providing low-latency and high throughput inference.
The teams from Kaskada and Redis focused on how iterate on amazing ML models with event-based data.
The team at Databricks talk about the motivations and use cases of feature stores across different industries.
The Rasgo and Prescient team will talk about the best setup, and what’s involved in getting features/models to be production-ready.
Richa Sachdev from Vanguard discusses the role of the feature store for MLOps for a successful analytical journey.
Moderated by Nicholas Pinckernell from Comcast.
AWS, Databricks, Rasgo, Kaskada, Vanguard and Shelf Engine dive into the specific benefits for data science and MLOps.
Atindriyo Sanyal from Galileo talks about data centric aspects of machine learning.
Josh Tobin from Gantry talks about the evaluation store and why to combine it with the feature store for more robust ML systems.
David Aronchick from Microsoft presents the Self-Assembling Machine Learning Environment, a new Kubernetes and Kubeflow project.
Laurel Orr from Stanford University discusses the challenges and opportunities with supporting embedding pipelines in feature store.
Patrick Urbanke from getML talks about how relational learning can be used to automate feature engineering, reducing time and costs.
Moderated by Chip Huyen from Stanford.
Microsoft, Stanford, getML, Galileo, and Gantry. discuss some of their predictions on what will become increasingly important in the coming months and years.
Robert Lock from Bosch discusses why companies shouldn't build their own solution when a software already exists as PaaS.
The team at Varo walks through the evolution of the feature store, from the ontological challenges to key functionalities.
Augusto Acioli from OLX presents a feature store where Data Scientists can create their online feature using queries.
The team at Uber talk about the advances of the Palette Feature Store that enables Feature Management at scale.
Cezar Steinz presents the ROI of the feature store according to the platform and models implementation at Via.
Renan from Wildlife Studios discusses how the Hopsworks Feature Store is helping them scale a centralized ML platform.
Moderated by Jim Dowling from Hopsworks.
Uber, Bosch, Varo, OLX, Via and Wildlife Studios discuss about what to consider when buying or building a feature store for ML.