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Past Recordings
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Reuse Features with Feature Views on Hopsworks
In this webinar, we will introduce the "feature view" from Hopsworks, which is the API for ML model development.
Kenneth Mak
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Real-time Machine Learning with Hopsworks - an integrated Feature Store and Model Serving platform
In this webinar, we will analyze the synergies of an integrated Feature Store and Model Serving platform for the operationalization of real-time ML-enabled services.
Javier de la Rúa Martínez
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How to Create Online Feature Pipelines
In this webinar, we will introduce the challenges of building online feature pipelines, including integration with a feature store, online model serving infrastructure, and real-time feature computation, with stream processing and on-demand features.
Jim Dowling
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Leverage your Snowflake, BigQuery, Redshift Data Warehouse with a Real-Time Feature Store
In this talk we will discuss how Hopsworks can be connected to existing cloud native data warehouses like Snowflake, Redshift and BigQuery.
Fabio Buso
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Data Validation for Enterprise ML using Great Expectations and Hopsworks Feature Store
In this webinar we will explain the core concepts of great expectations and how we made them available on Hopsworks to be used within your feature pipelines. Users are able to define expectation suites or reuse their existing ones.
Victor Jouffrey
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Great Models require great MLOps - using Weights and Biases with Hopsworks
Training an ML model nowadays is the easy part — managing the lifecycle of the experiments and the model is where things get complicated.
Moritz Meister
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Hopsworks 3.0, Introduction to the new Python-centric Feature Store
In this webinar, Fabio Buso, VP of Engineering at Hopsworks will present the new 3.0 release; How our new Feature View and write APIs have evolved to help Data Scientists bring their models to production, and other state-of-the art new improvements.
Fabio Buso
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Understanding performance and availability for feature stores
Join us to learn how performance at any scale is impacted by high availability infrastructures, and how those capabilities impact the value that is generated from the use of machine learning.
Jim Dowling
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Effective Data Collaboration in AI
Join us to learn how a feature store, with its multiple data sources and diversity of connectors, streaming data, and online data sets, allows teams to work together, share information, and complement each other's work.
Jim Dowling
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Effective Team Collaboration in AI
The modern data team consists of Data Engineers, Data Scientists, Machine Learning Engineers, and Business Analysts, but effective collaboration is challenging. In this talk we will show how a centralized data platform helps solve these issues.
Jim Dowling
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Installing Hopsworks Open Source
Learn how to install and configure Hopsworks on your local machines, and how to connect to the Hopsworks Feature Store.
Jim Dowling
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Embedding Stores and Feature Stores - Better Together
Learn how to build and operate search and recommendation systems on Hopsworks, with support for the most important pieces of infrastructure.
Jim Dowling
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Models and Features - More than the Sum of their Parts
Learn how to develop and deploy online models at ease for the open-source KServe and Hopsworks platforms without extensive MLOPs experience.
Jim Dowling
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Create your First Features
A live walk through on how to work with features, both offline and online. We will have a live coding demo on how to get started with Hopsworks, create features and push them to the Hopsworks Feature Store.
Jim Dowling
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