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.
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.
Additional Resources
Discover more AWS Machine Learning Competency Partner solutions and resources.
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General Resources
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Success Stories
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APN TV
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eBooks
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General Resources
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Maximizing Your Machine Learning Investment
Explore AWS Machine Learning Competency Partner eBooks, webinars, customer success stories, and more.
Machine Learning Foundations
Watch the on-demand webinars and discover how these technologies are essential for digital transformations.
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Success Stories
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Media & Entertainment
Cloudar Helps Ladrokes.live Transform The Sports Fan Experience With Cloud-Native Scalability
BelgiumLadbrokes.live, a provider of streaming sports entertainment content operating in Belgium, was grappling with critical challenges that hindered their ability to attract and retain users. Partnering with AWS Partner, Cloudar—also based in Belgium—the company transitioned to a cloud-native architecture powered by Amazon Web Services (AWS) technologies. This transformation enabled Ladbrokes.live to deliver a seamless, personalized user experience featuring avatars that can be customized to deliver information and statistics relevant to the viewers’ interests, in their language of choice. The solution also helped Ladbrokes.live optimize operational costs and set the stage for continuous innovation in a highly competitive industry.
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General Public Services
Wildlife-Centered AI: How AWS and Tehanu Use Generative AI to Give Wildlife a Voice in Global Conservation
RwandaThe Tehanu project in Rwanda’s Volcanoes National Park has demonstrated groundbreaking use of generative AI to infer and act on the interests of mountain gorillas. Leveraging technology solutions from Amazon Web Services (AWS), AWS Partner Anthropic, and with the support of AWS Partner Adastra, Tehanu created an automated pipeline to process behavioral data of gorillas, enabling the first-ever digital financial transactions by a non-human species. The AI solution synthesized vast academic and observational data, aligning conservation actions with species-specific preferences while supporting biodiversity efforts. This scalable, innovative approach sets a precedent for using AI to foster coexistence across species worldwide.
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APN TV
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eBooks
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AI Solutions for Financial Services
Appen’s artificial intelligence (AI) experts explain how to identify and implement successful machine learning and AI initiatives.
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.
Weave AI Into Your Business
Learn how to prepare for, embed, and put AI into production quickly to solve complex business problems.
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.
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Blogs
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Joshua Jin, Barry Ooi, Camilo Leon, Sudarshan Roy, 10/30/2024In Part 1 of this post, we covered how Retrieval Augmented Generation (RAG) can be used to enhance responses in generative AI applications by combining domain-specific information with a foundation model (FM). However, we stayed focused on the semantic search aspect of the solution, assuming that our vector store was already built and fully populated. In this post, we explore how to generate vector embeddings on Wikipedia data stored in a SQL Server database hosted on Amazon RDS. We also use Amazon Bedrock to invoke the appropriate FM APIs and an Amazon SageMaker Jupyter Notebook to help us orchestrate the overall process.
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Jagdeep Singh Soni, Aniketh Manjunath, Krishna Gourishetti, Sarthak Handa, Ishan Singh, Rupinder Grewal, 03/25/2025Amazon Bedrock announces the preview launch of Session Management APIs, a new capability that enables developers to simplify state and context management for generative AI applications built with popular open source frameworks such as LangGraph and LlamaIndex. Session Management APIs provide an out-of-the-box solution that enables developers to securely manage state and conversation context across [...]
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Melanie Li, Andrew Smith, Dustin Liu, June Won, Shikher Mishra, Vivek Gangasani, 03/25/2025In this post, we discuss the challenges faced by organizations when updating models in production. Then we deep dive into the new rolling update feature for inference components and provide practical examples using DeepSeek distilled models to demonstrate this feature. Finally, we explore how to set up rolling updates in different scenarios.
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Dean Capps, Feng Cai, Wajid Ali Mir, 03/25/2025In this post, we present an approach to using natural language processing (NLP) to query an Amazon Aurora PostgreSQL-Compatible Edition database. The solution presented in this post assumes that an organization has an Aurora PostgreSQL database. We create a web application framework using Flask for the user to interact with the database. JavaScript and Python code act as the interface between the web framework, Amazon Bedrock, and the database.
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Emily McKinzie, 03/25/2025The world’s most-watched sport, professional football (soccer) attracts a global fanbase of five billion people. With hundreds of thousands of players participating in matches across the world, the sport also generates staggering amounts of data, from goals to saves, assists, and beyond. In today’s connected world, fans want real-time access to all the match, player, [...]
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