{"product_id":"practical-mlops","title":"Practical MLOps: Operationalizing Machine Learning Models by Noah Gift and Alfredo Deza","description":"\u003ch2\u003ePractical MLOps: Operationalizing Machine Learning Models by Noah Gift and Alfredo Deza\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\"\u003ePractical MLOps\u003c\/i\u003e is that a machine learning model is completely useless until it is running in production and delivering value to a user. In the early waves of corporate data science, massive resources were spent training sophisticated models in Jupyter Notebooks, only for up to 90% of those models to never get deployed. Gift and Deza establish that this systemic failure is an operational bottleneck, not an algorithmic one. Data scientists focus on model accuracy, while operations teams focus on system uptime—and without a unified \u003cb data-path-to-node=\"6\" data-index-in-node=\"555\"\u003eMachine Learning Operations (MLOps)\u003c\/b\u003e framework, the bridge between them breaks.\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!----\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\u003cspan\u003eRather than diving into deep mathematical theories, the authors structure the book as a highly technical, end-to-end blueprint for automation. They guide readers through the practical mechanics of setting up fully automated build systems that compile, test, and containerize models the moment a data engineer updates an underlying training script. The text walks through major cloud environments—AWS, Azure, and Google Cloud Platform (GCP)—demonstrating how to leverage specialized tools like MLflow, KubeFlow, and cloud-native serverless functions to monitor data drift, manage model registries, and control cloud operational costs at scale.\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"30\"\u003eAs our regional software ecosystem undergoes an aggressive digital transformation, enterprise data groups, fintech firms, and retail tech platforms are collecting vast oceans of proprietary user data. However, many teams have run into a major roadblock: they can build machine learning prototypes, but they lack the operational infrastructure to scale them. Models remain trapped on developers' laptops, while manual cloud deployments frequently lead to broken integrations, high infrastructure bills, and untrustworthy performance in production.\u003c\/p\u003e\n\u003cp data-path-to-node=\"31\"\u003e\u003ci data-path-to-node=\"31\" data-index-in-node=\"0\"\u003ePractical MLOps\u003c\/i\u003e offers an incredibly clear, battle-tested playbook to break through these operational bottlenecks. Noah Gift and Alfredo Deza pool their immense corporate consulting and cloud-native system experiences into a clear, direct guide. It shows local engineering leads, cloud architects, and backend developers exactly how to build resilient, automated pipelines that turn raw code into high-availability cloud services. It is an essential read for any local technology professional ready to eliminate manual errors, maximize cloud resource efficiency, and ship secure, self-healing AI products that thrive under heavy production scale.\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: Systems 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","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47227432206521,"sku":null,"price":390.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/Practical_MLOps.jpg?v=1779263017","url":"https:\/\/royalbooksbd.com\/products\/practical-mlops","provider":"Royal Books BD","version":"1.0","type":"link"}