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Tag: Container registry

Accelerate ML workflows with Amazon SageMaker Studio Local Mode and Docker support | Amazon Web Services

We are excited to announce two new capabilities in Amazon SageMaker Studio that will accelerate iterative development for machine learning (ML) practitioners: Local Mode...

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Enhance Amazon Connect and Lex with generative AI capabilities | Amazon Web Services

Effective self-service options are becoming increasingly critical for contact centers, but implementing them well presents unique challenges. ...

Modernizing data science lifecycle management with AWS and Wipro | Amazon Web Services

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. ...

Run Kinesis Agent on Amazon ECS | Amazon Web Services

Kinesis Agent is a standalone Java software application that offers a straightforward way to collect and send data to Amazon Kinesis Data Streams and...

Unlocking the Power of Containers: Exploring the Top 20 Docker Containers for Every Development Need

Introduction Docker containers have emerged as indispensable tools in the fast-evolving landscape of software development and deployment, providing a lightweight and efficient way to package,...

Foundational data protection for enterprise LLM acceleration with Protopia AI | Amazon Web Services

This post is written in collaboration with Balaji Chandrasekaran, Jennifer Cwagenberg and Andrew Sansom and Eiman Ebrahimi from Protopia AI. ...

Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD | Amazon Web Services

Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is...

Build a medical imaging AI inference pipeline with MONAI Deploy on AWS | Amazon Web Services

This post is cowritten with Ming (Melvin) Qin, David Bericat and Brad Genereaux from NVIDIA. Medical imaging AI researchers and developers need a scalable,...

What Are the Best Practices for Deploying PySpark on AWS?

Introduction In big data and advanced analytics, PySpark has emerged as a powerful tool for processing large datasets and analyzing distributed data. Deploying PySpark on...

Dialogue-guided visual language processing with Amazon SageMaker JumpStart | Amazon Web Services

Visual language processing (VLP) is at the forefront of generative AI, driving advancements in multimodal learning that encompasses language intelligence, vision understanding, and processing....

Spark on AWS Lambda: An Apache Spark runtime for AWS Lambda | Amazon Web Services

Spark on AWS Lambda (SoAL) is a framework that runs Apache Spark workloads on AWS Lambda. It’s designed for both batch and event-based workloads,...

Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker | Amazon Web Services

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative...

Build and deploy ML inference applications from scratch using Amazon SageMaker | Amazon Web Services

As machine learning (ML) goes mainstream and gains wider adoption, ML-powered inference applications are becoming increasingly common to solve a range of complex business...

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