{"id":2939,"date":"2026-03-15T07:05:32","date_gmt":"2026-03-15T07:05:32","guid":{"rendered":"https:\/\/cloudzeninnovations.com\/dev\/?post_type=case-studies&amp;p=2939"},"modified":"2026-03-15T07:05:32","modified_gmt":"2026-03-15T07:05:32","slug":"medical-imaging-mlops-at-scale-aws-stack","status":"publish","type":"case_studies","link":"https:\/\/cloudzeninnovations.com\/dev\/case-studies\/medical-imaging-mlops-at-scale-aws-stack\/","title":{"rendered":"Medical Imaging MLOps at Scale ( AWS Stack)"},"content":{"rendered":"<p aria-level=\"3\"><b><span data-contrast=\"none\">1.Business\u00a0Challenge\u00a0:<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:281,&quot;335559739&quot;:281}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">A UK-based diagnostic imaging provider needed to operationalize\u00a0<\/span><b><span data-contrast=\"auto\">deep learning models for MRI and CT scan analysis<\/span><\/b><span data-contrast=\"auto\">\u00a0across multiple locations\u00a0and also\u00a0wanted to\u00a0develope\u00a0fully automated image processing pipelines on top of\u00a0Kubernetes and\u00a0Argo CD, Calra<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Challenges:<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"5\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"auto\">GPU-intensive workloads with inconsistent scaling<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"5\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"2\" data-aria-level=\"1\"><span data-contrast=\"auto\">Model performance drift across imaging devices<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"5\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"3\" data-aria-level=\"1\"><span data-contrast=\"auto\">Long inference times affecting diagnosis speed<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"5\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"4\" data-aria-level=\"1\"><span data-contrast=\"auto\">High cloud cost due to unmanaged GPU usage<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<ol start=\"2\">\n<li aria-level=\"3\"><b><span data-contrast=\"none\">CloudZenSolution\u00a0:<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:281,&quot;335559739&quot;:281}\">\u00a0<\/span><\/li>\n<\/ol>\n<p><span data-contrast=\"auto\">CloudZen implemented a\u00a0<\/span><b><span data-contrast=\"auto\">high-performance, cost-optimized MLOps platform on AWS<\/span><\/b><span data-contrast=\"auto\">.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Key capabilities:<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"6\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"auto\">Automated training pipelines for large image datasets<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"6\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"2\" data-aria-level=\"1\"><span data-contrast=\"auto\">GPU-optimized inference endpoints<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"6\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"3\" data-aria-level=\"1\"><span data-contrast=\"auto\">Canary deployments for new imaging models<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"6\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"4\" data-aria-level=\"1\"><span data-contrast=\"auto\">Performance and cost monitoring per model<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/li>\n<\/ul>\n<ol start=\"2\">\n<li><b><span data-contrast=\"auto\">Solution Provided byCloudZen\u00a0Innovations:<\/span><\/b><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ol>\n<p><span data-contrast=\"auto\">CloudZen\u00a0Innovations designed and implemented a secure, high-performance, and cost-optimized\u00a0MLOps\u00a0platform on AWS, purpose-built for large-scale healthcare imaging workloads. The solution enabled the customer to move from experimental AI models to production-grade, clinically reliable deployments while\u00a0maintaining\u00a0strict governance and cost control.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p aria-level=\"3\"><b><span data-contrast=\"none\">Key Capabilities Delivered<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:281,&quot;335559739&quot;:281}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">End-to-End Automated Training Pipelines<\/span><\/b><br \/>\n<span data-contrast=\"auto\">CloudZen\u00a0built fully automated ML pipelines to handle\u00a0large-scale medical image datasets (MRI, CT, X-ray). Data ingestion, preprocessing, model training, validation, and registration were orchestrated using AWS-native services, significantly reducing manual\u00a0intervention\u00a0and accelerating experimentation cycles.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">GPU-Optimized, Scalable Inference Architecture<\/span><\/b><br \/>\n<span data-contrast=\"auto\">To meet low-latency diagnostic requirements,\u00a0CloudZen\u00a0deployed\u00a0GPU-backed inference endpoints\u00a0with intelligent auto-scaling. This ensured consistent performance during peak diagnostic hours while avoiding over-provisioning during low-usage periods.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Safe Model Releases with Canary Deployments<\/span><\/b><br \/>\n<span data-contrast=\"auto\">New imaging models were rolled out using\u00a0canary deployment strategies, allowing a controlled percentage of traffic to be routed to newer models. This enabled real-world performance validation before full rollout, minimizing clinical risk and ensuring patient safety.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Model-Level Performance and Cost Observability<\/span><\/b><br \/>\n<span data-contrast=\"auto\">CloudZen\u00a0implemented continuous monitoring for\u00a0model accuracy, inference latency, data drift, and GPU\u00a0utilization. Cost and performance metrics were tracked at a per-model level, giving the customer complete visibility into operational efficiency and enabling proactive optimization.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-ccp-props=\"{}\"> <img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium wp-image-2940\" src=\"https:\/\/cloudzeninnovations.com\/dev\/dev\/wp-content\/uploads\/2026\/03\/11111-640x855.png\" alt=\"\" width=\"640\" height=\"855\" \/><\/span><\/p>\n<p>&nbsp;<\/p>\n<ol start=\"3\">\n<li><b><span data-contrast=\"none\"> Technology Stack:<\/span><\/b><\/li>\n<\/ol>\n<p><span data-contrast=\"auto\">This architecture\u00a0represents\u00a0a fully AWS-native\u00a0MLOps\u00a0platform designed to support scalable, secure, and automated machine learning workloads. Raw and curated datasets are stored in Amazon S3 and governed using AWS Glue Data Catalog, enabling schema management and metadata-driven data access. Analytical workloads and feature aggregation are supported through Amazon Redshift for high-performance querying.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Machine learning pipelines are built and orchestrated using\u00a0Amazon SageMaker, covering distributed training, experiment tracking, model versioning, and managed inference endpoints.\u00a0Amazon Kinesis\u00a0enables real-time data ingestion for streaming inference and near-real-time model evaluation.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">CI\/CD automation is implemented using\u00a0AWS CodePipeline, enabling controlled promotion of models across environments with built-in validation and approval stages. Continuous monitoring is achieved through\u00a0Amazon CloudWatch\u00a0and\u00a0SageMaker Model Monitor, providing visibility into infrastructure metrics, inference latency, data drift, and model quality.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Security and compliance are enforced across all layers using\u00a0AWS IAM\u00a0for fine-grained access control,\u00a0AWS KMS\u00a0for encryption of data at rest and in transit, and\u00a0AWS Shield\u00a0for infrastructure-level protection. The entire platform is provisioned and managed using\u00a0Terraform, ensuring reproducible, auditable, and scalable infrastructure deployments.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-ccp-props=\"{}\"> <img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium wp-image-2941\" src=\"https:\/\/cloudzeninnovations.com\/dev\/dev\/wp-content\/uploads\/2026\/03\/22222-640x427.png\" alt=\"\" width=\"640\" height=\"427\" \/><\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<ol start=\"4\">\n<li><b><span data-contrast=\"none\"> Business Outcome<\/span><\/b><\/li>\n<\/ol>\n<p><span data-contrast=\"auto\">By implementing a cloud-native MLOps platform on AWS, CloudZen enabled the customer to operationalize AI at scale with measurable business impact. The solution accelerated model deployment, improved diagnostic accuracy and reliability, reduced infrastructure and operational costs, and increased overall productivity\u2014transforming machine learning from isolated experiments into a dependable, production-grade capability.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335551550&quot;:1,&quot;335551620&quot;:1,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>1.Business\u00a0Challenge\u00a0:\u00a0 A UK-based diagnostic imaging provider needed to operationalize\u00a0deep learning models for MRI and CT scan analysis\u00a0across multiple locations\u00a0and also\u00a0wanted to\u00a0develope\u00a0fully automated image processing pipelines on top of\u00a0Kubernetes and\u00a0Argo CD,&hellip; <a href=\"https:\/\/cloudzeninnovations.com\/dev\/case-studies\/medical-imaging-mlops-at-scale-aws-stack\/\" class=\"read-more-link\">Read more<\/a><\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"footnotes":""},"case_studies_industry":[],"case_studies_service":[],"class_list":["post-2939","case_studies","type-case_studies","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cloudzeninnovations.com\/dev\/wp-json\/wp\/v2\/case_studies\/2939","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cloudzeninnovations.com\/dev\/wp-json\/wp\/v2\/case_studies"}],"about":[{"href":"https:\/\/cloudzeninnovations.com\/dev\/wp-json\/wp\/v2\/types\/case_studies"}],"version-history":[{"count":0,"href":"https:\/\/cloudzeninnovations.com\/dev\/wp-json\/wp\/v2\/case_studies\/2939\/revisions"}],"wp:attachment":[{"href":"https:\/\/cloudzeninnovations.com\/dev\/wp-json\/wp\/v2\/media?parent=2939"}],"wp:term":[{"taxonomy":"case_studies_industry","embeddable":true,"href":"https:\/\/cloudzeninnovations.com\/dev\/wp-json\/wp\/v2\/case_studies_industry?post=2939"},{"taxonomy":"case_studies_service","embeddable":true,"href":"https:\/\/cloudzeninnovations.com\/dev\/wp-json\/wp\/v2\/case_studies_service?post=2939"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}