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How iFood built a platform to run hundreds of machine learning models with Amazon SageMaker Inference

AWS Machine Learning Blog

The following diagram illustrates iFoods updated architecture, which incorporates an internal ML platform built to streamline workflows between data science and engineering teams, enabling efficient deployment of machine learning models into production systems. The ML platform empowers the building and evolution of ML systems.

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Prioritizing employee well-being: An innovative approach with generative AI and Amazon SageMaker Canvas

AWS Machine Learning Blog

In a single visual interface, you can complete each step of a data preparation workflow: data selection, cleansing, exploration, visualization, and processing. With a data flow, you can prepare data using generative AI, over 300 built-in transforms, or custom Spark commands. Choose Create. For Analysis name , enter a name.

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Accelerate pre-training of Mistral’s Mathstral model with highly resilient clusters on Amazon SageMaker HyperPod

AWS Machine Learning Blog

Similarly, a study by Meta AI and Carnegie Melon university found that, in the worst cases, 43 percent of compute time was wasted because of overheads due to hardware failures. This can adversely impact a customer’s ability to keep up with the pace of innovation in generative AI and can also increase the time-to-market for their models.

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How Forethought saves over 66% in costs for generative AI models using Amazon SageMaker

AWS Machine Learning Blog

This post is co-written with Jad Chamoun, Director of Engineering at Forethought Technologies, Inc. and Salina Wu, Senior ML Engineer at Forethought Technologies, Inc. Forethought is a leading generative AI suite for customer service. The following diagram illustrates our legacy architecture. 2xlarge instances.

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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning Blog

Optionally, if Account A and Account B are part of the same AWS Organizations, and the resource sharing is enabled within AWS Organizations, then the resource sharing invitation are auto accepted without any manual intervention. Following are the steps completed by using APIs to create and share a model package group across accounts.

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Migrating to Amazon SageMaker: Karini AI Cut Costs by 23%

AWS Machine Learning Blog

Karini AI , a leading generative AI foundation platform built on AWS, empowers customers to quickly build secure, high-quality generative AI apps. Depending on where they are in the adoption journey, the adoption of generative AI presents a significant challenge for enterprises.

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning Blog

The AWS portfolio of ML services includes a robust set of services that you can use to accelerate the development, training, and deployment of machine learning applications. The suite of services can be used to support the complete model lifecycle including monitoring and retraining ML models.