List of Amazon SageMaker Customers
Amazon Web Services (AWS)
410 Terry Avenue North,
Seattle, 98109-5210, WA,
United States
Seattle, 98109-5210, WA,
United States
1 206-266-1000
Professional Services
135000
$128.7B
Since 2010, our global team of researchers has been studying Amazon SageMaker customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Amazon SageMaker for ML and Data Science Platforms from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Amazon SageMaker for ML and Data Science Platforms include: JPMorgan Chase, a United States based Banking and Financial Services organisation with 317233 employees and revenues of $180.60 billion, Elevance Health, formerly Anthem, Inc, a United States based Insurance organisation with 104900 employees and revenues of $171.34 billion, Absa Group, a South Africa based Banking and Financial Services organisation with 60000 employees and revenues of $98.92 billion, Disney, a United States based Leisure and Hospitality organisation with 203000 employees and revenues of $82.71 billion, T-Mobile _x000D_, a United States based Communications organisation with 70000 employees and revenues of $81.40 billion and many others.
Contact us if you need a completed and verified list of companies using Amazon SageMaker, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the Machine Learning software purchases.
The Amazon SageMaker customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of Machine Learning software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Absa Group | Banking and Financial Services | 60000 | $98.9B | South Africa | Amazon Web Services (AWS) | Amazon SageMaker | ML and Data Science Platforms | 2021 | n/a |
In 2021, Absa Group implemented Amazon SageMaker as its ML and Data Science Platform. The Amazon SageMaker deployment was provisioned in the AWS cloud to provide managed model development, training, and hosted inference for Absa Group's data science teams.
Implementation centered on Amazon SageMaker capabilities including managed notebooks for experimentation, training jobs with automatic hyperparameter tuning, a model registry for versioning, pipeline orchestration for model CI CD, and hosted endpoints for real time inference. These modules were configured to support repeatable model development workflows and production model management.
The deployment integrated model outputs into Salesforce Sales Cloud on Absa Group's website to surface predictions and scores into sales workflows, enabling model outputs to be consumed where customer engagement occurs. Integration work focused on delivering inference payloads into Salesforce Sales Cloud and instrumenting model outputs for downstream operational use.
Governance incorporated model versioning in the registry, automated pipelines to promote validated models, and cloud native access controls to enforce separation of duties and auditability. Operationalization emphasized MLOps practices for reproducibility, monitoring, and staged rollout of hosted inference endpoints.
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Airbnb | Professional Services | 7300 | $11.1B | United States | Amazon Web Services (AWS) | Amazon SageMaker | ML and Data Science Platforms | 2017 | n/a |
In 2017, Airbnb implemented Amazon SageMaker as part of its ML and Data Science Platform work to scale text classification for customer service analytics. The deployment leveraged Amazon SageMaker Ground Truth Plus and AWS infrastructure to address high quality labeled data requirements for Mandarin language customer service logs, aligning applied machine learning teams and support operations around a repeatable labeling process.
The implementation centered on a customized data labeling workflow built with Amazon SageMaker Ground Truth Plus, paired with a custom text classification model developed and trained within Amazon SageMaker. Functional capabilities implemented included human-in-the-loop labeling orchestration, model-assisted labeling, and an automated model training and evaluation pipeline to support iterative improvements to classifier performance.
Operational coverage targeted one hundred thousand paragraphs of customer service logs in Mandarin, with the primary business functions impacted being customer support and Airbnbs ML and engineering teams responsible for applied models. The AWS team built the workflow to reduce manual dependency on the customer service organization while enabling ML teams to generate and maintain high quality training data at scale.
Governance and process changes included instituting a repeatable data labeling lifecycle with validation checkpoints and model-in-the-loop quality controls to maintain label accuracy. The program delivered a customized ML model that achieved 99% classification accuracy and supported Airbnbs goal to better serve customers and reduce dependencies on its customer service team.
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Ameriprise Financial | Banking and Financial Services | 13800 | $15.5B | United States | Amazon Web Services (AWS) | Amazon SageMaker | ML and Data Science Platforms | 2020 | n/a |
In 2020 Ameriprise Financial implemented Amazon SageMaker as its ML and Data Science Platform to support enterprise machine learning, advanced analytics, and model-driven reporting. The implementation anchored a broader initiative to design and maintain moderate to advanced data models and advanced reports used by internal and external stakeholders, with workstreams focused on text, speech, and image analysis and on building reproducible model training pipelines.
The Amazon SageMaker deployment was used for model development, training, and inference orchestration, integrating Python based feature engineering and Spark processing for large scale data preparation. Amazon SageMaker supported supervised, unsupervised, and reinforcement learning workflows, and was used alongside Spark on AWS components to develop and execute analytics and machine learning models for fraud detection and risk assessment. Model execution was embedded into production pipelines that included automated data ingestion, cleansing, transformation, and scheduling.
The architecture integrated Amazon SageMaker with AWS storage and compute services including Amazon S3 for data lakes, Amazon Redshift for data warehousing, Amazon EMR for Hadoop and Spark processing, and AWS Lambda for serverless ETL tasks with IAM roles and triggers via SQS and SNS. Glue and Athena were used as part of the serverless data pipeline and query layer, while streaming and batch components used Kafka, Storm, HDFS, and Spark Streaming for near real time learner data model ingestion. The broader environment also interfaced with Databricks and GCP BigQuery in proof of concept work, and supported downstream visualization in Power BI and SAS Visual Analytics.
Governance and operational controls were implemented through data profiling, data modelling, and data governance practices, with integration testing strategies for ETL jobs and CI CD pipelines built using Docker and GitHub. Job orchestration used Airflow scripts and Oozie workflows to schedule daily, weekly, and monthly jobs, and secure AWS landing zones and IAM role assignments enforced access and operational separation. The implementation included automation of data ingestion, monitoring of scheduled pipelines, and configuration of Spark clusters and high concurrency settings to support data science workloads.
Amazon SageMaker at Ameriprise Financial functioned as the central ML and Data Science Platform enabling model development and productionization for fraud detection and risk modeling, while supporting enterprise data warehouse and data lake use cases. The deployment tied model pipelines to reporting and analytics stacks, enabling consistent data preparation, model training, and inference processes across data engineering and data science teams.
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Anthropic | Professional Services | 2500 | $10.0B | United States | Amazon Web Services (AWS) | Amazon SageMaker | ML and Data Science Platforms | 2019 | n/a |
In 2019, Anthropic implemented Amazon SageMaker within its ML and Data Science Platforms portfolio to support model development workflows tied to Claude on Amazon Bedrock. Amazon SageMaker was provisioned to provide core ML platform capabilities for data scientists and ML engineers across research and product teams, and to centralize experiment and training operations.
The deployment emphasized Amazon SageMaker features for experiment tracking and model training orchestration, with explicit use of SageMaker Experiments to capture reproducible runs and artifact lineage. Configuration included automated training pipelines, hyperparameter tuning workflows, and model packaging steps suitable for controlled promotion to hosting or downstream deployment, reflecting standard ML and Data Science Platforms functional modules.
Integrations were implemented with AWS-hosted services, notably Amazon Bedrock for model serving and with Amazon SageMaker ML features like Experiments for lifecycle transparency. Architectural controls were oriented to keep data inside Anthropic's secure AWS environment, leveraging Bedrock data protections and AWS tooling to enable scalable, reliable, and secure AI application deployment while maintaining integration between SageMaker training and Bedrock inference pathways.
Governance centered on experiment reproducibility and model lifecycle oversight, with process changes to enforce experiment logging, artifact retention, and controlled promotion of models into Bedrock-hosted inference. The implementation emphasized secure data handling and platform-level integration, aligning Amazon SageMaker, Anthropic teams, and Amazon Bedrock within a governed ML operations framework.
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Artfinder | Retail | 50 | $5M | United Kingdom | Amazon Web Services (AWS) | Amazon SageMaker | ML and Data Science Platforms | 2017 | n/a |
In 2017 Artfinder implemented Amazon SageMaker to support its recommendation tooling within its AWS application stack. Artfinder implemented Amazon SageMaker as part of a broader ML architecture that operates on Amazon Web Services, augmenting existing services such as Amazon Machine Learning, Amazon Rekognition, and Amazon Kinesis Firehose to deliver personalized art recommendations, this work aligns with the ML and Data Science Platforms category.
The implementation centers on standard ML and data science platform capabilities including managed model training, automated model tuning, hosted inference endpoints, and batch scoring workflows. Amazon SageMaker was configured to consume image features derived from Amazon Rekognition and event streams captured through Amazon Kinesis Firehose, enabling feature engineering and model training pipelines that feed into recommendation models used by the marketplace.
Integrations are anchored to Artfinder's buyer facing website and merchandising workflows, with models served via SageMaker endpoints to deliver near real time and batch personalization across product discovery and customer recommendations. The operational scope includes the online marketplace connecting thousands of artists to buyers, with machine learning directly impacting customer experience and product discovery functions.
Governance for models emphasizes repeatable training and deployment pipelines common to ML and Data Science Platforms, with versioned models and automated deployment to inference endpoints to support iterative improvements. Artfinder’s use of Amazon SageMaker within the AWS ecosystem enables a structured ML lifecycle from feature ingestion through model hosting, production inference, and ongoing retraining, supporting the company goal of matching customers with art they will love.
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Transportation | 800 | $140M | United States | Amazon Web Services (AWS) | Amazon SageMaker | ML and Data Science Platforms | 2016 | Pariveda Solutions |
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Healthcare | 9000 | $2.7B | United States | Amazon Web Services (AWS) | Amazon SageMaker | ML and Data Science Platforms | 2019 | n/a |
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Professional Services | 50 | $7M | United States | Amazon Web Services (AWS) | Amazon SageMaker | ML and Data Science Platforms | 2017 | n/a |
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Construction and Real Estate | 12000 | $5.1B | United States | Amazon Web Services (AWS) | Amazon SageMaker | ML and Data Science Platforms | 2016 | Pariveda Solutions |
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Professional Services | 210 | $24M | United States | Amazon Web Services (AWS) | Amazon SageMaker | ML and Data Science Platforms | 2021 | n/a |
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Buyer Intent: Companies Evaluating Amazon SageMaker
ARTW Buyer Intent uncovers actionable customer signals, identifying software buyers actively evaluating Amazon SageMaker. Gain ongoing access to real-time prospects and uncover hidden opportunities. Companies Actively Evaluating Amazon SageMaker for ML and Data Science Platforms include:
- California Model & Design Group, a United States based Professional Services organization with 10 Employees
- Pokeepsie Films Spain, a Spain based Media company with 10 Employees
- The George Washington University Hospital, a United States based Healthcare organization with 2500 Employees
Discover Software Buyers actively Evaluating Enterprise Applications
| Logo | Company | Industry | Employees | Revenue | Country | Evaluated | ||
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FAQ - APPS RUN THE WORLD Amazon SageMaker Coverage
Amazon SageMaker is a ML and Data Science Platforms solution from Amazon Web Services (AWS).
Companies worldwide use Amazon SageMaker, from small firms to large enterprises across 21+ industries.
Organizations such as JPMorgan Chase, Elevance Health, formerly Anthem, Inc, Absa Group, Disney and T-Mobile _x000D_ are recorded users of
Amazon SageMaker for ML and Data Science Platforms.
Companies using Amazon SageMaker are most concentrated in Banking and Financial Services, Insurance and Leisure and Hospitality, with adoption spanning over 21 industries.
Companies using Amazon SageMaker are most concentrated in United States and South Africa, with adoption tracked across 195 countries worldwide. This global distribution highlights the popularity of Amazon SageMaker across Americas, EMEA, and APAC.
Companies using Amazon SageMaker range from small businesses with 0-100 employees - 13.04%, to mid-sized firms with 101-1,000 employees - 20.29%, large organizations with 1,001-10,000 employees - 31.88%, and global enterprises with 10,000+ employees - 34.78%.
Customers of Amazon SageMaker include firms across all revenue levels — from $0-100M, to $101M-$1B, $1B-$10B, and $10B+ global corporations.
Contact APPS RUN THE WORLD to access the full verified Amazon SageMaker customer database with detailed Firmographics such as industry, geography, revenue, and employee breakdowns as well as key decision makers in charge of ML and Data Science Platforms.