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Event

5:30 PM - 9:00 PM GMT
Tuesday, Dec 6
5:30 PM - 9:00 PM GMT
apply(recsys)
Join us for a free virtual event on data engineering and systems architecture for machine learning recommender systemsapply() is an event series for machine learning and data teams to discuss the practical data engineering challenges faced when building o
Mike Del Balso
Katrina Ni
Youlong Cheng
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Content

Workshop: Bring Your Models to Production with Ray Serve
video
In this workshop, we will walk through a step-by-step guide on how to deploy an ML application with Ray Serve. Compared to building your own model servers with Flask and FastAPI, Ray Serve facilitates seamless building and scaling to multiple models and serving model nodes in a Ray Cluster. Ray Serve supports inference on CPUs, GPUs (even fractional GPUs!), and other accelerators Ð using just Python code. In addition to single-node serving, Serve enables seamless multi-model inference pipelines (also known as model composition); autoscaling in Kubernetes, both locally and in the cloud; and integrations between business logic and machine learning model code. We will also share how to integrate your model serving system with feature stores and operationalize your end-to-end ML application on Ray.
Jun 1st, 2022 | Views 153
The dbt Semantic Layer
video
In this talk, Drew will discuss the dbt Semantic Layer and explore some of the ways that Semantic Layers and Feature Stores can be leveraged together to power consistent and precise analytics and machine learning applications.
Jun 1st, 2022 | Views 68
Enabling rapid model deployment in the healthcare setting
video
Discover how Vital powers its predictive, customer-facing, emergency department wait-time product with request-time input signals and how it solves its "cold-start" problem by building machine-learning feedback loops using Tecton.
May 12th, 2022 | Views 105
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