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Revolutionizing data management: Trends driving security, scalability, and governance in 2025

CIO

From data masking technologies that ensure unparalleled privacy to cloud-native innovations driving scalability, these trends highlight how enterprises can balance innovation with accountability. With machine learning, these processes can be refined over time and anomalies can be predicted before they arise.

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Building a Scalable ML Pipeline and API in AWS

Dzone - DevOps

With rapid progress in the fields of machine learning (ML) and artificial intelligence (AI), it is important to deploy the AI/ML model efficiently in production environments. The architecture downstream ensures scalability, cost efficiency, and real-time access to applications.

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Unlocking the Power of Serverless AI/ML on AWS: Expert Strategies for Scalable and Secure Applications

Dzone - DevOps

Amazon Web Services (AWS) provides an expansive suite of tools to help developers build and manage serverless applications with ease. In this article, we delve into serverless AI/ML on AWS, exploring best practices, implementation strategies, and an example to illustrate these concepts in action.

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Build a video insights and summarization engine using generative AI with Amazon Bedrock

AWS Machine Learning - AI

This engine uses artificial intelligence (AI) and machine learning (ML) services and generative AI on AWS to extract transcripts, produce a summary, and provide a sentiment for the call. Organizations typically can’t predict their call patterns, so the solution relies on AWS serverless services to scale during busy times.

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Extend large language models powered by Amazon SageMaker AI using Model Context Protocol

AWS Machine Learning - AI

For MCP implementation, you need a scalable infrastructure to host these servers and an infrastructure to host the large language model (LLM), which will perform actions with the tools implemented by the MCP server. We will deep dive into the MCP architecture later in this post.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning - AI

API Gateway is serverless and hence automatically scales with traffic. The advantage of using Application Load Balancer is that it can seamlessly route the request to virtually any managed, serverless or self-hosted component and can also scale well. It’s serverless so you don’t have to manage the infrastructure.

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Build and deploy a UI for your generative AI applications with AWS and Python

AWS Machine Learning - AI

Traditionally, building frontend and backend applications has required knowledge of web development frameworks and infrastructure management, which can be daunting for those with expertise primarily in data science and machine learning. The full code of the demo is available in the GitHub repository.