Amazon SageMaker
9 use cases using this technology
Attensi Uses the Power of AI to Reduce the Time It Takes to Develop Content by up to 95%
Attensi
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.
Aizon Improves Pharmaceutical Manufacturing With Artificial Intelligence Using AWS
Aizon
Aizon built a GxP-compliant AI platform on AWS to help pharmaceutical manufacturers optimize biomanufacturing processes. Using AWS IoT, AWS Lambda, and Amazon SageMaker, Aizon created digital twins of bioreactors that trigger machine learning operations for process optimization. For a multinational pharmaceutical company producing blood-plasma-based drugs, Aizon built unsupervised and supervised learning models that identified two critical process variables; optimizing them improved yield by double digits, worth potentially tens of millions of dollars. AWS also reduced Aizon's compute costs by up to 80 percent.
Pfizer saves 16,000 hours annually with generative AI search through the PACT initiative
Pfizer
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.
Siemens Electronics Factory Erlangen Reduces Machine Learning Deployment Time by 80% with AWS and Siemens Industrial AI on Industrial Edge
Siemens Electronics Factory Erlangen
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.
Foxintelligence Enhances Ecommerce Insight Using AWS and Generative AI
Foxintelligence
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.
CDPHP modernizes infrastructure and improves medical data extraction with AWS AI/ML
CDPHP (Capital District Physicians' Health Plan Inc.)
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.
Mercedes-Benz Consulting builds AI-powered digital assistant for natural-language document search
Mercedes-Benz
Mercedes-Benz Consulting transformed how employees access information by building an AI-powered digital assistant that processes natural language queries. Using Amazon SageMaker and AWS Lambda, the platform significantly improved knowledge access across millions of documents.
Workday Accelerates Generative AI / ML Product Development Using Amazon SageMaker
Workday
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.
Novartis: Accelerating Drug Development with AI-Powered Clinical Trial Transformation
Novartis
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.