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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. Wipro is an AWS Premier Tier Services Partner and Managed Service Provider (MSP).

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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

It allows you to seamlessly customize your RAG prompts and retrieval strategies—we provide the source attribution, and we handle memory management automatically. To enable effective retrieval from private data, a common practice is to first split these documents into manageable chunks. Choose Next. Choose Next.

APIs 107
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Generative AI, LLMs and AI Assistants: A Deep Dive into Customer Experience Technology

COPC

Related CX Technology Consulting Fusing technology and expertise to design and deliver exceptional service journeys. Crafting LLM AI Assistants: Roles, Process and Timelines Using the latest AI may seem as easy as developers using APIs in commercial LLM options like OpenAI. Developing an LLM AI assistant involves multiple ingredients.

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Reduce the time taken to deploy your models to Amazon SageMaker for testing

AWS Machine Learning

SageMaker is a fully managed machine learning (ML) service. Additionally, you don’t need to manage servers. The SageMakerMigration class consists of high-level abstractions over SageMaker APIs that significantly reduce the steps needed to deploy your model to SageMaker, as illustrated in the following figure.

Scripts 73
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Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing

AWS Machine Learning

Applications and services can call the deployed endpoint directly or through a deployed serverless Amazon API Gateway architecture. To learn more about real-time endpoint architectural best practices, refer to Creating a machine learning-powered REST API with Amazon API Gateway mapping templates and Amazon SageMaker.

Scripts 91
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Deploy generative AI models from Amazon SageMaker JumpStart using the AWS CDK

AWS Machine Learning

Fargate is a technology that you can use with Amazon ECS to run containers without having to manage servers or clusters or virtual machines. The web application interacts with the models via Amazon API Gateway and AWS Lambda functions as shown in the following diagram. View the deployed resources on the AWS Management Console.

APIs 89
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Use Amazon SageMaker Data Wrangler in Amazon SageMaker Studio with a default lifecycle configuration

AWS Machine Learning

Lifecycle configurations (LCCs) are shell scripts to automate customization for your Studio environments, such as installing JupyterLab extensions, preloading datasets, and setting up source code repositories. LCC scripts are triggered by Studio lifecycle events, such as starting a new Studio notebook. Apply the script (see below).

Scripts 79