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Generate training data and cost-effectively train categorical models with Amazon Bedrock

AWS Machine Learning

In this post, we explore how you can use Amazon Bedrock to generate high-quality categorical ground truth data, which is crucial for training machine learning (ML) models in a cost-sensitive environment. This results in an imbalanced class distribution for training and test datasets.

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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

Customers can use the SageMaker Studio UI or APIs to specify the SageMaker Model Registry model to be shared and grant access to specific AWS accounts or to everyone in the organization. We will start by using the SageMaker Studio UI and then by using APIs.

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Considerations for addressing the core dimensions of responsible AI for Amazon Bedrock applications

AWS Machine Learning

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

APIs 108
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Unlocking insights and enhancing customer service: Intact’s transformative AI journey with AWS

AWS Machine Learning

This allowed Intact to transcribe customer calls accurately, train custom language models, simplify the call auditing process, and extract valuable customer insights more efficiently. The goal was to refine customer service scripts, provide coaching opportunities for agents, and improve call handling processes.

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Secure a generative AI assistant with OWASP Top 10 mitigation

AWS Machine Learning

Contrast that with Scope 4/5 applications, where not only do you build and secure the generative AI application yourself, but you are also responsible for fine-tuning and training the underlying large language model (LLM). These steps might involve both the use of an LLM and external data sources and APIs.

APIs 103
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Generate customized, compliant application IaC scripts for AWS Landing Zone using Amazon Bedrock

AWS Machine Learning

Organizations typically counter these hurdles by investing in extensive training programs or hiring specialized personnel, which often leads to increased costs and delayed migration timelines. Traditionally, cloud engineers learning IaC would manually sift through documentation and best practices to write compliant IaC scripts.

Scripts 126
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Accelerate Mixtral 8x7B pre-training with expert parallelism on Amazon SageMaker

AWS Machine Learning

By utilizing sparse expert subnetworks that process different subsets of tokens, MoE models can effectively increase the number of parameters while requiring less computation per token during training and inference. This enables more cost-effective training of larger models within fixed compute budgets compared to dense architectures.

APIs 128