Feature Store 101

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class="base-main-card__title font-sans text-[18px] font-bold text-color-text overflow-hidden "> Provectus </h3> <h4 class="base-main-card__subtitle body-text text-color-text overflow-hidden "> We help businesses leverage cloud, data, and AI to reimagine the way they operate, compete, and deliver customer value. </h4>  <div class="body-text text-color-text-low-emphasis base-main-card__metadata"> Published Jul 25, 2022 </div>  </div> <div class="base-main-card__ctas z-[3] self-center ml-3 babybear:ml-1 babybear:self-start"> <a class="base-main-card__cta btn-sm btn-secondary" href="https://www.linkedin.com/signup/cold-join?session_redirect=%2Fpulse%2Ffeature-store-101-provectus-it-inc%3Ftrk%3Darticle-ssr-frontend-pulse_more-articles_related-content-card&trk=article-ssr-frontend-pulse_publisher-author-card" data-tracking-control-name="article-ssr-frontend-pulse_publisher-author-card" data-tracking-will-navigate> + Follow </a> </div> </div> </div> </section> </header> <div data-test-id="article-content-blocks">  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>AI & ML have reached an inflection point. In 2020, organizations in almost every industry began evolving their AI/ML projects from experimentation to production on a larger scale. While doing so, they realized they were wasting a lot of time and effort on feature definition and extraction. The team at </span><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fprovectus%2Ecom%2F&urlhash=lcRH&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>Provectus </a></span><span class> has built and worked with various feature stores, and we would like to share our experience in this article.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <h3><span class>WHAT IS A FEATURE STORE?</span></h3> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">Feature Store</span><span class> is a data management layer for ML features and a data transformation service that enables users to manipulate and store raw data as ready-to-use features for any ML model.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <h3><span class>WHY SHOULD ORGANIZATIONS IMPLEMENT FEATURE STORES?</span></h3> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>A Feature Store is a fundamental component of the ML stack and data infrastructure that </span><span class="font-[700]">enables efficient feature engineering and management through:</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class> <ul> <li><span class>Simple reuse of features</span></li> <li><span class>Feature standardization</span></li> <li><span class>Feature consistency between offline and online models</span></li> </ul> </span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Centralized and scalable feature stores allow businesses to </span><span class="font-[700]">innovate faster</span><span class> and </span><span class="font-[700]">drive ML processes at scale</span><span class>.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <h3><span class>MAJOR BENEFITS OF FEATURE STORES FOR BUSINESSES</span></h3> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class> <ul> <li><span class="font-[700]">Better ROI from feature engineering through reduced cost per model.</span><span class> More effective and cost-efficient feature engineering facilitates collaboration, sharing, and reusing of features.</span></li> <li><span class="font-[700]">Faster time to market for new models, driven by increased productivity of ML Engineers.</span><span class> Storage implementation and features serving API are decoupled from ML engineers, allowing them to focus on models, not on latency problems, for more efficient online serving.</span></li> </ul> </span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <h3><span class>ARE FEATURE STORES REQUIRED FOR EVERY AI/ML USE CASE?</span></h3> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Feature stores are </span><span class="font-[700]">only necessary for </span><span class="font-[700]"><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fprovectus%2Ecom%2Fmachine-learning-infrastructure%2F&urlhash=_f7r&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>Data & ML Infrastructure </a></span><span class> in projects where models need a stateful, ever-changing representation of the system.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">Examples of such AI/ML use cases:</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class> <ul> <li><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fprovectus%2Ecom%2Fdemand-forecasting-retail%2F&urlhash=lu9i&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>Demand forecasting </a></span></li> <li><span class>Personalization & Recommendation engines</span></li> <li><span class>Dynamic pricing optimization</span></li> <li><span class>Supply chain optimization</span></li> <li><span class>Fraud detection </span></li> <li><span class>Predictive maintenance</span></li> </ul> </span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <h3><span class>MAJOR CONCEPTS OF A STANDARDIZED FEATURE STORE</span></h3> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class> <ul> <li><span class="font-[700]">Online Feature Store.</span><span class> Online applications look for a feature vector that is sent to an ML model for predictions</span></li> <li><span class="font-[700]">ML Specific Metadata.</span><span class> Enables the discovery and reuse of features</span></li> <li><span class="font-[700]">ML Specific API and SDK.</span><span class> High-level operations for fetching training feature sets and online access</span></li> <li><span class="font-[700]">Materialized Versioned Datasets.</span><span class> Maintains versions of feature sets used to train ML models</span></li> </ul> </span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <h3><span class>WHAT MAJOR FEATURE STORES ARE AVAILABLE ON THE MARKET?</span></h3> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class> <ul> <li><span class="font-[700]">Tecton.</span><span class> An enterprise-ready feature store that manages the complete lifecycle of features — from engineering of new features to serving them online for real-time predictions.</span></li> <li><span class="font-[700]">Feast.</span><span class> Feast helps teams bridge the gap between data and machine learning models, allowing them to register, ingest, serve, and monitor features in production.</span></li> <li><span class="font-[700]">Amazon SageMaker Feature Store.</span><span class> A fully managed, purpose-built repository to store, update, retrieve, and share machine learning (ML) features.</span></li> <li><span class="font-[700]">Hopsworks.</span><span class> An enterprise platform for the development and operation of ML models, available as an on-premises platform and as a managed platform on AWS and Azure.</span></li> <li><span class="font-[700]">Molecula.</span><span class> A cloud-first feature store that covers a wide range of project needs, from data readiness to MLOps. Molecula promises an entirely new paradigm for continuous real-time data analysis.</span></li> </ul> </span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <h3><span class>WHAT ARE THE MAJOR FEATURE STORES? — TECTON</span></h3> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]"><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fwww%2Etecton%2Eai%2F&urlhash=I4wm&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>Tecton </a></span><span class> is an enterprise-ready feature store that manages the complete lifecycle of features — from engineering of new features to serving them online for real-time predictions.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">Key Benefits.</span><span class> Enterprise-grade capabilities, including:</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class> <ul> <li><span class>Feature Monitoring</span></li> <li><span class>Versioning Management</span></li> <li><span class>Collaboration</span></li> <li><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fprovectus%2Ecom%2Fdata-quality-assurance%2F&urlhash=zH_g&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>Data Quality </a></span></li> </ul> </span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>The Tecton platform can be used in various large-scale machine learning scenarios and supports such </span><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fprovectus%2Ecom%2Fmachine-learning-operations-mlops%2F&urlhash=0Jvo&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>MLOps </a></span><span class> stacks as Amazon SageMaker, Kubeflow, and Databricks.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">Delivery Mode:</span><span class> SaaS </span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">Early Adopters:</span><span class> Atlassian, Tide, Omdia</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <h3><span class>WHAT ARE THE MAJOR FEATURE STORES? — FEAST</span></h3> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]"><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Ffeast%2Edev%2F&urlhash=-WKn&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>Feast </a></span><span class> is an open source feature store that helps Data Scientists and ML Engineers bridge the gap between data and ML models, allowing them to register, ingest, serve, and monitor features in production.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">Key Benefits:</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class> <ul> <li><span class>Open source, simple, and lightweight</span></li> <li><span class>Support of offline/online feature capturing</span></li> <li><span class>Part of the Linux Foundation for AI & Data</span></li> </ul> </span> </p> </div> <div class="inline-articles bg-color-surface-new border-hidden rounded-sm p-1 mx-auto my-1.5 mamabear:w-2/3 inline-articles__after"> <h2 class="text-xl font-medium ml-1.5 my-1 text-color-text mamabear:text-center">Recommended by LinkedIn</h2> <div class="flex flex-wrap mx-0.5 babybear:mx-1.5"> <div class="content-author-card flex flex-col max-w-[270px] h-auto rounded-md border-1 border-system-gray-30 border-solid pb-1.5 inline-article mx-auto my-1 border-hidden papabear:w-[219px] babybear:min-w-full mamabear:min-w-full h-auto bg-color-canvas-tint"> <a class="no-underline" data-tracking-control-name="inline-recommended-articles" data-tracking-will-navigate href="https://www.linkedin.com/pulse/ai-ml-data-mesh-unleashing-datas-potential-people-tech-group-inc-bz1lc"> <img class="content-image w-full h-[150px] bg-cover rounded-t-md object-cover" data-delayed-url="https://media.licdn.com/dms/image/v2/D5612AQGCWe-ZchGx3Q/article-cover_image-shrink_720_1280/article-cover_image-shrink_720_1280/0/1724664197357?e=2147483647&v=beta&t=pZdCxGHS84Mn4gr_pRD9LxicUOvghmrpU57TwzyhmMM"> <div class="inline-article-title text-md text-color-text font-bold pl-2 pt-1 pr-1.5">AI, ML, and Data Mesh: Unleashing Data's Potential</div> <div class="inline-article-meta px-2 mt-1 font-sans text-xs font-normal text-color-text-low-emphasis h-full"> <span class="inline-article-author pt-0.5"> People Tech Group Inc </span> <span class="before:middot pt-0.5 inline-article-published-date"> 2 months ago </span> </div> </a>  </div> <div class="content-author-card flex flex-col max-w-[270px] h-auto rounded-md border-1 border-system-gray-30 border-solid pb-1.5 inline-article mx-auto my-1 border-hidden papabear:w-[219px] babybear:min-w-full mamabear:min-w-full h-auto bg-color-canvas-tint"> <a class="no-underline" data-tracking-control-name="inline-recommended-articles" data-tracking-will-navigate href="https://www.linkedin.com/pulse/ai-modern-platforms-drive-data-transformation-nousinfosystems-zpm3c"> <img class="content-image w-full h-[150px] bg-cover rounded-t-md object-cover" data-delayed-url="https://media.licdn.com/dms/image/v2/D5612AQEi7S907vQqhQ/article-cover_image-shrink_720_1280/article-cover_image-shrink_720_1280/0/1718374472204?e=2147483647&v=beta&t=SXg_AufGt6OstjP2jcvgi8_WCZfBkC-MRsmC5jKvw8w"> <div class="inline-article-title text-md text-color-text font-bold pl-2 pt-1 pr-1.5">AI and Modern Platforms Drive Data Transformation</div> <div class="inline-article-meta px-2 mt-1 font-sans text-xs font-normal text-color-text-low-emphasis h-full"> <span class="inline-article-author pt-0.5"> Nous Infosystems </span> <span class="before:middot pt-0.5 inline-article-published-date"> 5 months ago </span> </div> </a>  </div> <div class="content-author-card flex flex-col max-w-[270px] h-auto rounded-md border-1 border-system-gray-30 border-solid pb-1.5 inline-article mx-auto my-1 border-hidden papabear:w-[219px] babybear:min-w-full mamabear:min-w-full h-auto bg-color-canvas-tint"> <a class="no-underline" data-tracking-control-name="inline-recommended-articles" data-tracking-will-navigate href="https://www.linkedin.com/pulse/transforming-business-ai-data-centers-factories-guide-mark-minevich-td72e"> <img class="content-image w-full h-[150px] bg-cover rounded-t-md object-cover" data-delayed-url="https://media.licdn.com/dms/image/v2/D4E12AQFoAplfLv_JJg/article-cover_image-shrink_720_1280/article-cover_image-shrink_720_1280/0/1726698702133?e=2147483647&v=beta&t=uL0juADJ9LFwcX8YML2QC4Av-ZlhLgkd7HffRaWeVN0"> <div class="inline-article-title text-md text-color-text font-bold pl-2 pt-1 pr-1.5">Transforming Business with AI Data Centers "AI…</div> <div class="inline-article-meta px-2 mt-1 font-sans text-xs font-normal text-color-text-low-emphasis h-full"> <span class="inline-article-author pt-0.5"> Mark Minevich </span> <span class="before:middot pt-0.5 inline-article-published-date"> 2 months ago </span> </div> </a>  </div> </div> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Feast serves as a lightweight model for capturing offline and online features in ML pipelines, and supports such MLOps stacks as Spark and Kubeflow.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">Delivery Mode:</span><span class> Cloud and On-premise</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">Early Adopters:</span><span class> Google, GoJek, Zulily, Agoda, Farfetch</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <h3><span class>WHAT ARE THE MAJOR FEATURE STORES? — AMAZON SAGEMAKER FEATURE STORE</span></h3> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]"><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Faws%2Eamazon%2Ecom%2Fru%2Fsagemaker%2Ffeature-store%2F&urlhash=bOtC&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>Amazon SageMaker Feature Store </a></span><span class> is a fully managed, purpose-built repository for machine learning features. Seamlessly integrated with the SageMaker platform, it enables the search, discovery, and management of ML features at scale.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">Key Benefits:</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class> <ul> <li><span class>Native integration with SageMaker and other AWS services</span></li> <li><span class>Easy entry point for DS/ML teams already working on the SageMaker platform</span></li> </ul> </span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Though Amazon SageMaker Feature Store's UX lags behind other platforms, its native integration with Amazon SageMaker makes it the feature store of choice for AWS-based projects.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">Delivery Mode:</span><span class> Amazon Web Services (AWS)</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">Early Adopters:</span><span class> Intuit, Experian, DeNA, Care.com</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <h3><span class>WHAT ARE THE MAJOR FEATURE STORES? — HOPSWORKS</span></h3> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]"><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fwww%2Ehopsworks%2Eai%2F&urlhash=2E_P&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>Hopsworks </a></span><span class> is an open-source feature store for developing and operating AI applications. It includes key building blocks for feature lifecycle management: feature discovery, analysis, and serving.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">Key Benefits:</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class> <ul> <li><span class>Integration with various MLOps stacks (Databricks, SageMaker, Azure ML), database and streaming platforms</span></li> <li><span class>Integration with most Python libraries for ingestion and training</span></li> <li><span class>Support of offline feature stores with time travel</span></li> </ul> </span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Hopsworks provides a middle ground between Feast and Tecton, although it relies on the HopsML infrastructure and may not satisfy all latency requirements.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">Delivery Mode:</span><span class> On-premise and Cloud (AWS, Azure, GCP)</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">Early Adopters:</span><span class> Nvidia, AMD, SCANIA</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <h3><span class>WHAT ARE THE MAJOR FEATURE STORES? — MOLECULA</span></h3> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]"><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fwww%2Emolecula%2Ecom%2F&urlhash=Qnb3&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>Molecula </a></span><span class> is a cloud-first feature store that covers a wide range of AI/ML project needs, from data readiness to MLOps.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">Key Benefits:</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class> <ul> <li><span class>Designed for both large-scale and streaming data processing</span></li> <li><span class>Integration with various databases and streaming platforms</span></li> <li><span class>Introduces Data Taps to abstract specific data sources for feature extraction, to enable feature modeling on SQL queries</span></li> </ul> </span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>The Molecula feature store platform is heavily geared towards enterprises to enable seamless feature extraction across multiple databases and streaming messaging platforms.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">Delivery Mode:</span><span class> On-premise</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">Early Adopters:</span><span class> Q2 eBanking</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <h3><span class>MOVING FORWARD WITH FEATURE STORE</span></h3> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>It is still early in the game for feature stores, but organizations that are moving beyond experimentation to production of machine learning projects have already realized the need for a centralized repository to store, update, retrieve, and share features.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Have any questions? Feel free to reach out to Provectus at </span><span class><a href="https://www.linkedin.com/redir/redirect?url=http%3A%2F%2Fmailto%3Asales%40provectus%2Ecom&urlhash=amrc&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>sales@provectus.com </a></span><span class> and learn more about </span><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fprovectus%2Ecom%2Fwebinar-feature-store-as-data-foundation-for-ml-nov-2020%2F&urlhash=wpgG&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>implementing a feature store as an integral part of your ML lifecycle </a></span><span class>. And don't forget to share your vision of feature stores with us!</span> </p> </div>  </div>   </article>  <code id="ugcPostUrl" style="display: none"></code> <code id="ugcPost" style="display: none"></code> <code id="disableLinkedinShareRedirectLink" style="display: none"></code> <code id="ugcPostUrn" style="display: none"></code> <code id="signInUrl" style="display: none"></code>  <div class="article-social-activity z-[201] x-social-activity-wrapper xs:sticky bottom-0 flex justify-center border-t-1 border-solid border-color-border-faint bg-color-background-container " data-impression-id="article-ssr-frontend-pulse_x-social-details">  <div class="x-social-activity social-details x-social-activity-left flex flex-wrap w-full max-w-x-article-width-small justify-between lg:max-w-x-article-width"> <div class="flex x-social-activity-left flex-wrap"> <code id="i18n_reaction_selection_state" style="display: none"></code> <div class="reactions-menu flex flex-row relative items-center 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