In machine learning, features are the context that allows models to make the best possible decisions. Working with well-defined, up-to-date, shareable features is how your organization develops supremely effective algorithms.
Join each week as we talk to MLOps operators, practitioners, and professionals about the current state of MLOps.
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Find more details on what the FeatureForm workflow can do for you, as well as what sets it apart from rival feature store platforms.
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We already support a wide range of data infrastructure and ML platforms, including Amazon SageMaker, Databricks, and Kubeflow. Our goal is meeting you where you are and making the implementation of our Virtual Feature Store incredibly simple and valuable, so we’re constantly working to expand our available integrations.
The only thing you’ll need to do for Featureform to utilize your existing Features is append them with a snippet of proprietary code.
If you’re using models that depend on accurate, specialized Features, you’ll want a way to organize, manage, and track those Features over time. A Feature Store is built for that purpose, while a Virtual Feature Store offers the exact same functionality in a lightweight package. Create more reliable models, and iterate faster than ever before.
You can click here to set up a demo of the Featureform platform, or email us at [email protected] so we can answer your most specific questions. Either way, we look forward to hearing from you soon!
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