AWS Machine Learning Competency Partners

Drive innovation and unlock greater business value with AWS Specialization Partners that have deep technical knowledge and proven customer success

AWS Machine Learning Competency Partners have demonstrated expertise delivering machine learning (ML) solutions on the AWS Cloud. These partners offer a range of services and technologies to help you create intelligent solutions for your business, from enabling data science workflows to enhancing applications with machine intelligence.

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Data processing such as ingestion, consolidation, removal of duplicate records, imputation of missing values, scaling/normalization of values, elimination of correlated features, feature engineering, and others.

No and low-code platforms for machine learning, usually with a predominantly visual interface, that enable end-to-end model development.

High-code solutions, RESTful API, GraphQL, and algorithms that provide access to trained models and components used to train models.

AWS ML Competency Partners have demonstrated expertise in helping organizations solve the most challenging problems in AI, including data engineering, data science, machine and deep learning, and production deployment for inference at scale.

Development, deployment, and maintenance of ML applications that positively impact customer business outcomes and add value on top of AWS services, in particular AWS AI Services, to solve specific customer needs.

Continuous integration and continuous deployment solutions for ML models over the entire data lifecycle including data lake creation, automated data preprocessing across data services, deployment in the cloud, and machine-learning-specific rules and processes for model redeployment.

Connect with AWS Machine Learning Competency Partners

Drive innovation, meet business objectives, and get the most out of your AWS services by partnering with technically validated AWS Partners.

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Additional Resources

Discover more AWS Machine Learning Competency Partner solutions and resources.

  • General Resources
  • Maximizing Your Machine Learning Investment

    Explore AWS Machine Learning Competency Partner eBooks, webinars, customer success stories, and more.

    View the AWS Machine Learning Partner Resources »

    Machine Learning Foundations

    Watch the on-demand webinars and discover how these technologies are essential for digital transformations.

    View AWS Machine Learning Partner Foundations »

  • Success Stories
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    • Financial Services

      Paynela Improves Data Accessibility Using generative AI With the Help of Mission

      United States

      Paynela, a Puerto Rico-based healthcare financing innovator, revolutionized patient financial assistance through cutting-edge solutions powered by AWS Partner Mission. Dedicated to making healthcare more accessible, Paynela helps patients manage their out-of-pocket medical expenses with dignity and ease. By leveraging Amazon Web Services (AWS) and Mission's expertise, Paynela harnessed the power of generative AI to analyze complex healthcare data, resulting in deeper insights and expanded patient support capabilities.

      2025
    • Software & Internet

      NeuralSpace Accelerates AI Model Training Speed by 96% in Migration to AWS with Rebura

      United Kingdom

      NeuralSpace, a London-based AI startup, had the same problem that many startups have: not enough time, not enough money, and too much to do. It needed to develop and train the AI models that powered its language AI applications—automatic translation of text and speech, automated subtitling, and automated AI dubbing of content—but these processes were taking too long. With 20–30 TB of data being used to train each model, it could take 3–6 months to train just one. And the company needed to train multiple models to develop its products. NeuralSpace knew that it needed to find a way to speed up model training that would fit within its limited budget. With the help of AWS Partner Rebura, NeuralSpace migrated to Amazon Web Services (AWS) to enable faster modeling and a crucial pivot in focus.

      2024
    • Manufacturing

      Usiminas, with Enkel’s Support, Reduces R$ 9 Million in Transportation Costs by Optimizing Processes Through AWS Services

      Brazil

      Usiminas, supported by AWS Partner Enkel tackled significant challenges in freight pricing and route optimization within the steel industry. By developing a customized automation platform based on AWS services, the Brazilian steel company streamlined logistics processes, leading to a 14% reduction in costs and significant efficiency gains. The project not only cut delivery distances by 448,000 kilometers but also resulted in a notable decrease in CO2 emissions, avoiding the release of 1,577 metric tons. This project enhances Usiminas' market competitiveness and reinforces its commitment to environmental sustainability, demonstrating the positive impact of innovative solutions in the logistics sector.

      2024
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  • APN TV
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  • eBooks
  • AI Solutions for Financial Services

    Appen’s artificial intelligence (AI) experts explain how to identify and implement successful machine learning and AI initiatives.

    View the eBook »

    Machine Learning Within Reach

    Learn how to connect with Amazon SageMaker to develop, test, and deploy machine models at scale and take advantage of cost-effective, pay-as-you-go pricing.

    View the eBook »

    Weave AI Into Your Business

    Learn how to prepare for, embed, and put AI into production quickly to solve complex business problems.

    View the eBook »

    Mining Your Data Lake for Analytics Insights

    Learn about using Delta Lake on Databricks and AWS to prepare and deliver data that drives valuable analytics insights.

    View the eBook »

  • Blogs
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    • Carlie Marvel, 01/17/2025
      In January 2024, we welcomed the first cohort of learners of AWS Cloud Institute, a comprehensive program designed to equip aspiring cloud builders with the skills needed for high-demand roles in cloud technology. Today, we’re thrilled to celebrate the graduation of this inaugural cohort of AWS Cloud Institute learners, marking a significant milestone in their journey toward launching successful careers in cloud development. Read how the program changed the lives of so many new cloud professionals.
    • Fei Lang, Kiran Randhi, Amit Kumar Sharma, Rita Ku, 01/17/2025
      Discover how Publicis Sapient’s new AI-powered marketing solution combines margin prediction and offer generation capabilities on AWS to revolutionize campaign creation. Using Amazon Bedrock and advanced LLMs, marketers can now generate data-driven, personalized campaigns in minutes instead of days, while accurately predicting campaign performance and optimizing ROI.
    • Alfred Shen, Anya Derbakova, 12/19/2024
      This post provides step-by-step instructions for creating a collaborative multi-agent framework with reasoning capabilities to decouple business applications from FMs. It demonstrates how to combine Amazon Bedrock Agents with open source multi-agent frameworks, enabling collaborations and reasoning among agents to dynamically execute various tasks. The exercise will guide you through the process of building a reasoning orchestration system using Amazon Bedrock, Amazon Bedrock Knowledge Bases, Amazon Bedrock Agents, and FMs. We also explore the integration of Amazon Bedrock Agents with open source orchestration frameworks LangGraph and CrewAI for dispatching and reasoning.
    • Bill Screen, Chris Rach, 01/27/2025
      In this post, you will discover the key challenges public sector agencies face when it comes to real-time data access, and how Amazon Bedrock Agents and Powertools for AWS Lambda work together to address these challenges. The post also includes real-world use cases demonstrating practical applications in the public sector and technical implementation details and best practices.
    • Aidan Ricci, Charlotte Fondren, Nate Haynes, 01/27/2025
      This post walks you through the end-to-end process of deploying a single custom model on SageMaker using NASA’s Prithvi model. The Prithvi model is a first-of-its-kind temporal Vision transformer pre-trained by the IBM and NASA team on contiguous US Harmonised Landsat Sentinel 2 (HLS) data. It can be finetuned for image segmentation using the mmsegmentation library for use cases like burn scars detection, flood mapping, and multi-temporal crop classification.

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