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An AWS service, available since 2017, is foundational for today’s popular generative AI models.
Amazon SageMaker launched in 2017 and has been steadily iterated on in the years since. While much of the limelight and attention in the gen AI world at AWS over the last year has been focussed on Amazon Bedrock, Amazon SageMaker continues to offer a critical set of capabilities.
Amazon SageMaker is an AWS service for managing the entire machine learning lifecycle, from building and training models to deploying and managing predictive models at scale. It provides a managed environment and tools for customers to build, train, and deploy machine learning and deep learning models. Hundreds of thousands of customers are using Amazon SageMaker for tasks like training popular gen AI models and deploying machine learning workloads. Amazon SageMaker is used as a service that helped to train Stability AI’s Stable Diffusion and it is the machine learning framework that helped to enable the Luma’s Dream Machine text to video generator.
AWS is now expanding the capabilities further with the general availability of the managed MLflow on SageMaker service. MLflow is a popular open source platform for the machine learning lifecycle, including experimentation, reproducibility, deployment and monitoring of machine learning models. With the availability of managed MLFlow for Amazon SageMaker, AWS is giving its users more power and choice for building the next generation of AI models.
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Join enterprise leaders in San Francisco from July 9 to 11 for our flagship AI event. Connect with peers, explore the opportunities and challenges of Generative AI, and learn how to integrate AI applications into your industry. Register Now
“Given the current pace of innovation in the space, our customers are looking to move quickly from experimentation to production, and really accelerate time …
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It’s time to celebrate the incredible women leading the way in AI! Nominate your inspiring leaders for VentureBeat’s Women in AI Awards today before June 18. Learn More
An AWS service, available since 2017, is foundational for today’s popular generative AI models.
Amazon SageMaker launched in 2017 and has been steadily iterated on in the years since. While much of the limelight and attention in the gen AI world at AWS over the last year has been focussed on Amazon Bedrock, Amazon SageMaker continues to offer a critical set of capabilities.
Amazon SageMaker is an AWS service for managing the entire machine learning lifecycle, from building and training models to deploying and managing predictive models at scale. It provides a managed environment and tools for customers to build, train, and deploy machine learning and deep learning models. Hundreds of thousands of customers are using Amazon SageMaker for tasks like training popular gen AI models and deploying machine learning workloads. Amazon SageMaker is used as a service that helped to train Stability AI’s Stable Diffusion and it is the machine learning framework that helped to enable the Luma’s Dream Machine text to video generator.
AWS is now expanding the capabilities further with the general availability of the managed MLflow on SageMaker service. MLflow is a popular open source platform for the machine learning lifecycle, including experimentation, reproducibility, deployment and monitoring of machine learning models. With the availability of managed MLFlow for Amazon SageMaker, AWS is giving its users more power and choice for building the next generation of AI models.
VB Transform 2024 Registration is Open
Join enterprise leaders in San Francisco from July 9 to 11 for our flagship AI event. Connect with peers, explore the opportunities and challenges of Generative AI, and learn how to integrate AI applications into your industry. Register Now
“Given the current pace of innovation in the space, our customers are looking to move quickly from experimentation to production, and really accelerate time …nnDiscussion:nn” ai_name=”RocketNews AI: ” start_sentence=”Can I tell you more about this article?” text_input_placeholder=”Type ‘Yes'”]