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Amazon SageMaker

The next generation of Amazon SageMaker is an integrated platform for analytics and AI, bringing together SageMaker AI (model development, training and deployment), SageMaker Unified Studio, SageMaker Catalog (built on Amazon DataZone) and a lakehouse architecture that unifies Amazon S3 data lakes and Amazon Redshift data warehouses with governance built in.

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23published use cases
14industries
7countries on record
Generative AI
top AI capability

Evidence mix: High 23 · Medium 0 · Low 0 — bands are computed from each record's evidence signals.

Industry
Country

23 use cases

AutomotiveComputer VisionSpeech & Audio AIPredictive Analytics

BMW Group uses AI-powered vision and sound analytics for predictive maintenance and quality management

The BMW Group built its Vision and Sound Analytics Service (VSAS) on AWS, processing 1.3 million image and audio files daily across 1.3 PB of data at more than 16 production sites worldwide. The company uses the platform's stored data to build AI models for predictive maintenance and quality management, including a computer vision model that predicts potential rail failures to reduce downtime, and an Acoustic Analytics application that automatically captures and analyzes vehicle sounds using trained models to detect anomalies and improve engine performance and in-cabin comfort, reducing the need for human intervention in testing vehicles with no driver present. Using AWS Graviton processors for AI model training, BMW cut compute costs for its computer vision solution by 63% and reduced model training time for various use cases from days to hours.

BMW GroupAmazon EC2 · Amazon S3 · Amazon OpenSearch Service +4
Life SciencesComputer VisionMachine Learning

Novo Nordisk uses machine learning and computer vision to automate pharmaceutical manufacturing quality checks

Novo Nordisk, a Danish pharmaceutical company that supplies nearly 50 percent of the world's insulin, built an automated ML pipeline on AWS using Amazon SageMaker Pipelines to train, deploy, and monitor computer vision models on edge devices. The system automates previously manual tasks including counting drug cartridges in boxes and detecting bacterial anomalies on agar plates, using a robotic arm, camera rig, and edge inference, with results monitored via an Amazon QuickSight dashboard. The solution deploys ML models at scale to different edge devices, automates quality-assurance tasks, improves time to market, and was successfully repurposed from the cartridge-counting use case to the agar-plate anomaly detection use case with minimal changes.

Novo Nordisk· DenmarkAmazon SageMaker · Amazon S3 · AWS IoT Greengrass +1
Financial ServicesGenerative AIComputer VisionDocument Intelligence

Empowering Employees to Work Strategically Using Amazon Bedrock with BDM

Big Data Mining (BDM), a Brazilian company, built LOUIS, a bespoke generative AI model on Amazon Bedrock combining computer vision and natural language processing to process complex, unstructured corporate documents (contracts, powers of attorney) across more than 100 models and 15 industries. A large Brazilian financial institution deployed LOUIS to replace a team of 150 professionals manually evaluating over 40,000 unstructured processes per month for opening legal-entity accounts and contracting credit, standardizing interpretation criteria and reducing operational effort by more than 40%, an estimated $4.2 million in cost savings over five years. In insurance, LOUIS reduced life insurance claim processing from up to six months to just a few minutes by capturing required data in about 60 seconds. BDM reports documents are processed 85% faster than manual interpretation with 98% accuracy, and saved roughly 50% on development costs by building on Amazon Bedrock rather than writing the application from scratch.

Big Data Mining (BDM)· BrazilAmazon Bedrock · Amazon S3 · Amazon EC2 +1
Technology & SoftwareGenerative AIConversational AI

Attensi Uses the Power of AI to Reduce the Time It Takes to Develop Content by up to 95%

Norwegian gamified-training software company Attensi enhanced its Creator authoring tool and simulation training platform with generative AI built on AWS services including Amazon Bedrock, Amazon SageMaker, and Amazon EKS, plus AWS Partner Hugging Face. The AI features generate dialogue, tasks and minigames, translate text, and convert text to speech, cutting content-creation time by 90-95% with Creator's AI copilots. Attensi's aggregate customer data shows game-based training drives a 19% productivity increase, 5x faster time to competence, 85% knowledge-gap closure, 2.5x revenue growth, and 75% attrition reduction.

Attensi· NorwayAmazon Bedrock · Amazon SageMaker · Amazon Elastic Kubernetes Service (EKS) +1
HealthcareGenerative AIMachine LearningFraud & Anomaly Detection

Pfizer saves 16,000 hours annually with generative AI search through the PACT initiative

Pfizer and AWS created the Pfizer-Amazon Collaboration Team (PACT) initiative in 2021, pursuing 14 rapid-prototyping projects applying AWS analytics, ML and generative AI to laboratory, clinical manufacturing and supply chain challenges. Using Amazon Kendra and, later, Anthropic's Claude 2.1 via Amazon Bedrock (accessed through an internal voice/chatbot platform called Vox), Pfizer estimates scientists save up to 16,000 hours of document search time annually and cut related infrastructure costs by 55%. A separate PACT prototype uses Amazon SageMaker, Amazon Lookout for Equipment and Amazon Lookout for Metrics to detect anomalies in Pfizer's continuous manufacturing (PCMM) process. Five of the initial 14 PACT projects have moved into production.

Pfizer· United StatesAmazon Kendra · Amazon Bedrock · Amazon SageMaker +2
ManufacturingComputer VisionFraud & Anomaly Detection

Siemens Electronics Factory Erlangen Reduces Machine Learning Deployment Time by 80% with AWS and Siemens Industrial AI on Industrial Edge

Siemens Electronics Factory Erlangen, which manufactures PCBs and controllers such as SINAMICS converters and SINUMERIK CNC controllers, used computer vision models to spot anomalies in PCB assembly, but training and retraining ML models on premises was time-intensive and constrained by GPU bottlenecks. The factory adopted AWS services together with Siemens Industrial Edge and Siemens Industrial AI, sending shopfloor images via Edge applications to Amazon S3 before training via Amazon SageMaker or AWS Lambda; training results and edge prediction results are monitored through AI Model Monitor, with AI Model Manager providing central management of models on the shopfloor. This reduced time spent on model training and retraining by 80 percent (from about 30 minutes to roughly 5 minutes for retraining and deployment), cut costs by more than 90 percent compared to on-premises data storage systems, and reduced the false call rate by over 50 percent, while continuously preventing around 4 percent of PCB assembly errors compared to around 60 percent at peaks in the past.

Siemens Electronics Factory ErlangenAmazon S3 · Amazon SageMaker · AWS Lambda +5
E-commerceMachine LearningGenerative AI

Foxintelligence Enhances Ecommerce Insight Using AWS and Generative AI

Foxintelligence by NielsenIQ, the largest ecommerce measurement and consumer analytics company in Europe with around 5 million active online buyers tracked, uses Amazon SageMaker to build, train, and deploy its machine learning models and is exploring Amazon Bedrock to access foundation models, fine-tune them, and do prompt engineering, reducing time to market for generative AI innovation. As a company handling consumer personal data, Foxintelligence relies on AWS security features to comply with the EU's General Data Protection Regulation (GDPR) and other regional regulations as it expands globally, with advice from the AWS engineering team on new compliance features.

FoxintelligenceAmazon SageMaker · Amazon Bedrock
HealthcareNatural Language ProcessingMachine LearningDocument Intelligence

CDPHP modernizes infrastructure and improves medical data extraction with AWS AI/ML

CDPHP, a not-for-profit health plan serving 400,000 members in Upstate New York, used AWS services including Amazon Comprehend Medical, Amazon Textract, and Amazon SageMaker to automate its data processing pipeline for unstructured medical records and health data. The organization processed over seven million records during initial migration and now processes 3,000 electronic health records weekly. CDPHP achieved a 60% improvement in overall efficiency and reduced HEDIS report generation from 4-5 days (three data scientists) to two reports produced daily.

CDPHP (Capital District Physicians' Health Plan Inc.)· United StatesAmazon Comprehend Medical · Amazon Textract · Amazon SageMaker
Human ResourcesLarge Language ModelsDocument IntelligenceRecommendation & Personalization

Workday Accelerates Generative AI / ML Product Development Using Amazon SageMaker

Workday uses Amazon SageMaker to let engineering teams build, train and deploy ML models, including LLMs, across AWS Regions to meet global customers' data-residency requirements. Using Amazon SageMaker Studio, Jumpstart and Ground Truth Plus, Workday piloted a closed-book ML application analyzing job descriptions, invoices and contracts, improving ML inference latency by a factor of five. Workday also received early access to Amazon Bedrock for generative AI prototyping. Head of Workday AI Shane Luke said the company has scaled from a thousand inference requests to tens of millions coming in daily.

WorkdayAmazon SageMaker · Amazon SageMaker Studio · Amazon SageMaker Jumpstart +2
Life SciencesDocument IntelligenceRetrieval-Augmented GenerationAgentic AI

Novartis: Accelerating Drug Development with AI-Powered Clinical Trial Transformation

Novartis partnered with AWS Professional Services and Accenture to modernize their drug development infrastructure and integrate AI across clinical trials, with the goal of reducing trial development cycles by at least six months. The initiative built a GXP-compliant data mesh platform on AWS with Databricks for processing, enabling AI use cases including protocol generation and an intelligent decision system (digital twin). Early results from the patient safety domain showed 72% query speed improvements, 60% storage cost reduction, and 160+ hours of manual work eliminated. The protocol generation use case achieved 83-87% acceleration in producing compliant protocols.

NovartisAmazon Web Services (AWS) · Databricks · Amazon S3 +8