How to Join the Feature Store For ML Community and Access Its Resources

Updated

Featurestore.org (Feature Store For ML) is a community and learning hub for data science professionals, researchers, and engineers working with feature stores. You can join its Slack community, attend monthly meetups, read blog posts and the MLOps dictionary, watch videos and summit recordings, and contribute your own content. Most resources are organized through the site's navigation and its Slack and Meetup channels; the site does not publish pricing or login requirements for these resources, so treat access details as things to confirm on the linked pages.

Join the Slack Community

The site describes itself as "a global community of data science professionals, researchers and engineers that enjoy sharing ideas and learning from each other." The entry point is the Join our Slack link in the site navigation.

  • Input: the Join our Slack link on the site.
  • Action: follow it to the Slack invitation flow.
  • Expected result: access to the community Slack server, where members exchange ideas and knowledge about feature stores and the surrounding data and AI environment.

The site does not specify invitation approval steps or workspace rules, so check the invitation page for any conditions.

Attend Monthly Meetups

The site states that it organizes monthly meetups with industry experts and leading companies in the Data and AI field. The Meetups page lists upcoming events.

  • Input: the Meetups link in the navigation.
  • Action: open the page and review the upcoming event listings.
  • Expected result: you can see scheduled meetups and register through the linked event page.

The site does not list specific dates, locations, or formats (in-person vs. online) in the available material, so the Meetups page is the place to confirm those details.

Use the Learning Resources

The site organizes its learning material into several sections, each reachable from the main navigation:

Resource What it covers Where to find it
Blog Featured posts on streamlining ML development with a feature store, feature pipeline and system engineering tradeoffs, and serverless ML Blog section
What Is a Feature Store Introductory material on the concept "What Is a Feature Store" page
MLOps Dictionary Terminology for the MLOps and feature store space MLOps Dictionary page
Videos Recorded talks and presentations Videos section
Serverless ML course A course on serverless machine learning Serverless ML – Course link

The blog's featured posts are attributed to named authors (John Thomas, Assaf Pinhasi, Lex Avstreikh), which helps you judge the perspective before reading. The site also invites readers to contribute: "Have an interesting blog idea? Contribute."

Watch Summit and Meetup Recordings

The site hosts recordings from the Feature Store Summit across multiple years (2021–2025) and from past meetups. These are listed under the Summit and Meetup Recordings sections.

  • Input: the Feature Store Summit or Meetup Recordings links.
  • Action: select a year or event.
  • Expected result: access to recorded talks you can watch on demand.

This is useful if you cannot attend a live meetup or want to review past summit content.

Explore the Benchmarks and Advanced Guide

Beyond community content, the site publishes technical comparisons:

  • Benchmarks: Offline API, Online API, and Feature Freshness benchmarks, listed in the navigation.
  • Feature Store Advanced Guide 2025: a vendor comparison covering in-house, open-source, on-prem, and vendor options. The available excerpt describes entries such as Hopsworks (described as the first open-source feature store and the first with a DataFrame API, supporting batch and streaming sources and stream processing for writes, available as a managed platform and on-premises) and Google's Vertex AI Feature Store (a centralized repository on GCP supporting BigQuery and GCS as sources, with BigQuery for offline and BigTable for online serving). The guide also references SageMaker Feature Store's integration with AWS services such as Redshift.

If your goal is to evaluate tools rather than join the community, start with the Advanced Guide and benchmarks; if your goal is learning and networking, start with Slack and the meetups.

A Practical Starting Path

For a new visitor who wants to get involved quickly:

  1. Open the Meetups page and note the next event.
  2. Use Join our Slack to enter the community and ask questions there.
  3. Read What Is a Feature Store and skim the MLOps Dictionary to build shared vocabulary.
  4. Watch one Feature Store Summit recording to see how practitioners describe their systems.
  5. If you have something to share, use the Contribute link to propose a blog post.

The site does not state whether any of these resources require payment or account creation beyond the Slack and Meetup flows, so verify on each linked page before committing time.