July 2021 Summaries

This is apply(meetup), a virtual event for ML practitioners on August 11th from 9am to 12pm Pacific Time, designed for sharing and discussing the latest state of ML data engineering in a free and virtual format. The event features a packed lineup of great speakers, including Karan Goel, Rand Xie, Aparna Dhinakaran, Emily Hawkins, Matt Delacour, James Campbell, and Willem Pienaar, who will share their insights on topics such as building malleable ML systems, feature store development, ML observability, streaming architecture, and more. The event is entirely virtual and free to attend, with the opportunity for participants to join the apply() Slack channel and continue the conversation after the event.

Online inference can significantly increase the impact of a machine learning model by incorporating fresh data, but it requires careful engineering to ensure a snappy user experience. To keep latency low during online inference, smaller or faster models and unifying feature pipelines are key considerations. Tecton's Feature Store makes it easy for teams to serve fresh feature data for their online models, allowing Data Scientists to write a single feature definition that can be used in both offline model training and online model inference. The Feature Store simplifies the process of joining together batch, streaming, and request-time data by providing separate features based on their data source through Feature Views, which then define the set of features for the model in a Feature Service.