{"product_id":"building-machine-learning-systems-with-a-feature-store","title":"Building Machine Learning Systems with a Feature Store: Batch, Real-Time, and LLM Systems by Jim Dowling","description":"\u003ch2\u003eBuilding Machine Learning Systems with a Feature Store: Batch, Real-Time, and LLM Systems by Jim Dowling\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003eThe core thesis of \u003ci data-path-to-node=\"6\" data-index-in-node=\"19\"\u003eBuilding Machine Learning Systems with a Feature Store\u003c\/i\u003e is that operational machine learning is fundamentally a data engineering challenge, not an algorithmic modeling challenge. In most enterprise tech teams, data scientists spend up to 80% of their time writing repetitive data transformation code to clean features for training. Worse, when these models go live, software backend engineers are forced to rewrite those exact same data pipelines in a different language (like Java or C++) to serve real-time predictions. This structural fragmentation leads to catastrophic \u003cb data-path-to-node=\"6\" data-index-in-node=\"592\"\u003etraining-serving skew\u003c\/b\u003e, where a model performs beautifully in training but fails instantly in production due to mismatched data calculations.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003cspan class=\"text-block-with-attachment\"\u003e\u003cspan class=\"attachment-container search-images\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003cspan class=\"text-block-with-attachment\"\u003e\u003cspan\u003eJim Dowling establishes the modern \u003cb data-path-to-node=\"7\" data-index-in-node=\"35\"\u003eFeature Store\u003c\/b\u003e as the ultimate technical architectural layer that bridges this divide. Acting as a specialized data warehouse for machine learning, a feature store provides a dual-database design: a \u003cb data-path-to-node=\"7\" data-index-in-node=\"233\"\u003eTime-Travel Offline Store\u003c\/b\u003e optimized for processing massive, cost-efficient batch data for model training, and a \u003cb data-path-to-node=\"7\" data-index-in-node=\"345\"\u003eLow-Latency Online Store\u003c\/b\u003e (operating at sub-millisecond speeds) to serve real-time feature variables during live production inferences. Dowling walks readers through building complete F-O-M (Feature, Training, Serving) pipelines, structuring streaming features using Apache Kafka and Flink, and utilizing the feature store as a robust semantic foundation to power Large Language Model (LLM) agents via real-time context injections.\u003c\/span\u003e\u003c\/span\u003e\u003ccode role=\"text\" data-test-id=\"code-content\" class=\"code-container formatted ng-tns-c3299913081-66 no-decoration-radius\"\u003e\u003c\/code\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003cspan class=\"text-block-with-attachment\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cdiv class=\"code-block ng-tns-c3299913081-66 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahgKEwjvvJOByMeUAxUAAAAAHQAAAAAQqgE\"\u003e\n\u003c!----\u003e\n\u003cdiv class=\"formatted-code-block-internal-container ng-tns-c3299913081-66\"\u003e\n\u003cdiv class=\"animated-opacity ng-tns-c3299913081-66\"\u003e\n\u003c!----\u003e\n\u003cp data-path-to-node=\"30\"\u003eAs our regional tech ecosystem enters a sophisticated phase of scaling automated banking apps, personalized e-commerce engines, and high-throughput fintech platforms, local companies are running into a painful structural roadblock. While training an AI model in a Jupyter Notebook is relatively easy, keeping that model supplied with clean, real-time, unpolluted data feeds in production is notoriously difficult. Many local engineering teams watch their expensive AI models fail on deployment because they lack the data platform architecture needed to feed their systems accurately on autopilot.\u003c\/p\u003e\n\u003cp data-path-to-node=\"31\"\u003e\u003ci data-path-to-node=\"31\" data-index-in-node=\"0\"\u003eBuilding Machine Learning Systems with a Feature Store\u003c\/i\u003e provides the precise, infrastructure-level blueprint to break through this multi-million dollar bottleneck. Jim Dowling uses his immense prestige as an enterprise system creator to deliver a highly practical, code-rich masterclass. He demonstrates exactly how to decouple fragile code blocks, construct reliable offline\/online data lakes, and bridge the dangerous gap between data engineering and machine learning teams. It is an absolutely essential playbook for any local CTO, MLOps professional, or data engineer determined to control cloud infrastructure expenses, eliminate pipeline errors, and build bulletproof, real-time intelligent software that scales flawlessly.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Data Platform Engineering.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"31\"\u003e \u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47228083142841,"sku":null,"price":390.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/Building_Machine_Learning_Systems_with_a_Feature_Stor.jpg?v=1779271451","url":"https:\/\/royalbooksbd.com\/products\/building-machine-learning-systems-with-a-feature-store","provider":"Royal Books BD","version":"1.0","type":"link"}