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Amazon Bedrock Custom Model Import now generally available

AWS Machine Learning

This feature empowers customers to import and use their customized models alongside existing foundation models (FMs) through a single, unified API. This unified experience enables developers to use the same tooling and workflows across both base FMs and imported custom models. Be backed by enterprise grade security and privacy tooling.

APIs 138
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Accenture creates a regulatory document authoring solution using AWS generative AI services

AWS Machine Learning

This solution uses the SageMaker JumpStart AI21 Jurassic Jumbo Instruct and AI21 Summarize models to extract and create the documents. The following diagram illustrates the solution architecture. The workflow consists of the following steps: A user accesses the regulatory document authoring tool from their computer browser.

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models. Data science and DevOps teams may face challenges managing these isolated tool stacks and systems. SageMaker comes with the necessary tools for scaling a model during inference.

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Streamline Sales Processes with Enterprise CPQ

Cincom

CPQ solutions enhance sales effectiveness by simplifying quoting, centralizing product data, recommending optimal deals, and automating repetitive administrative workflows. Core CPQ features include: Guided Selling Tools: CPQ applies configuration requirements and logic when building customized quotes.

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Configure an AWS DeepRacer environment for training and log analysis using the AWS CDK

AWS Machine Learning

With the advancements in automation and configuring with increasing levels of abstraction to set up different environments with IaC tools, the AWS CDK is being widely adopted across various enterprises. He works with AABG to develop and implement innovative cloud solutions, and specializes in infrastructure as code and cloud security.

Scripts 84
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Automated exploratory data analysis and model operationalization framework with a human in the loop

AWS Machine Learning

The tools and technologies to assist with data preprocessing have been growing over the years. Now we have low-code and no-code tools like Amazon SageMaker Data Wrangler , AWS Glue DataBrew , and Amazon SageMaker Canvas to assist with data feature engineering. The following figure illustrates our Step Function workflow.

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Multilingual content processing using Amazon Bedrock and Amazon A2I

AWS Machine Learning

Document processing stages Our reference solution uses a highly resilient pipeline, as shown in the following diagram, to coordinate the various document processing stages. A predefined JSON schema can be provided to the Rhubarb API, which makes sure the LLM generates data in that specific format.

APIs 92