Machine Learning Platforms

Prepare your data, train, manage, and deploy your models with Machine Learning Platforms.

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Data preparation, operationalize ML and AutoML

Machine Learning Platforms enable data science professionals to source and prepare data, build, deploy, and train models, and uncover actionable insights to improve business outcomes. AWS Marketplace solutions, along with Amazon Machine Learning services, can help organizations across multiple industries, leverage machine learning algorithms to simplify data experimentation.

Learn how to discover and deploy NVIDIA’s Triton Inference Server to run an object detection service.

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Solutions

The following solutions from AWS Marketplace sellers focus on Amazon Sagemaker for model delivery.

Cloudera

Cloudera Data Platform (CDP) accelerates predictive decision making from research to production with a secure, scalable, and open platform for ML.

See how it works:
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Databricks

Databricks enables organizations to manage all their data, analytics, and artificial intelligence on one unified data platform.

See how it works:
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Domino

Domino’s enterprise MLOps platform accelerates data science, providing self-service infrastructure so teams can develop and productionize models faster.

Qubole

Qubole's Open Data Lake Platform for Machine Learning allows you deliver faster results by building, visualizing, and collaborating on ML models.

See how it works:
See how it works ➜

Benefits

Build ML applications faster

Plug and play pre-trained machine learning models from third-party sellers across various use cases without sharing the data outside your environment.

Reduce time for data prep

Use pre-trained models and solutions that can help identify high-quality data from your overall data set suitable for training the ML models.

Simple feature engineering

Leverage models and solutions to identify and extract additional features from your data that can be used to train your own downstream models.
TIVO
"The Qubole interface makes it easy for our developers to go to a notebook, pick a cluster, and get started with a query. They don't have to worry about managing the cluster, and they're able to collaborate with other developers easily by sharing notebooks."

—Lucas Waye, Principal Engineer, Tivo

Resources

Learn about the latest practices on how to analyze data to obtain deeper insights with resources from AWS Marketplace.
Learn how to simplify ML predictions as self-service for business users with minimal ML background.
Read blog 

Learn how to build and deploy ML models using Amazon SageMaker Autopilot.

Read blog 
Learn how to discover and deploy NVIDIA’s Triton Inference Server to run an object detection service.
Read blog