AWS Clustering System Architecture 
AWS Machine Learning Blog
MARCH 10, 2025
We walk through the journey Octus took from managing multiple cloud providers and costly GPU instances to implementing a streamlined, cost-effective solution using AWS services including Amazon Bedrock, AWS Fargate , and Amazon OpenSearch Service. Along the way, it also simplified operations as Octus is an AWS shop more generally.
AWS Machine Learning Blog
DECEMBER 4, 2024
Orchestrate with Tecton-managed EMR clusters – After features are deployed, Tecton automatically creates the scheduling, provisioning, and orchestration needed for pipelines that can run on Amazon EMR compute engines. You can view and create EMR clusters directly through the SageMaker notebook.
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AWS Machine Learning Blog
SEPTEMBER 18, 2024
The compute clusters used in these scenarios are composed of more than thousands of AI accelerators such as GPUs or AWS Trainium and AWS Inferentia , custom machine learning (ML) chips designed by Amazon Web Services (AWS) to accelerate deep learning workloads in the cloud.
AWS Machine Learning Blog
FEBRUARY 24, 2023
AWS recently released Amazon SageMaker geospatial capabilities to provide you with satellite imagery and geospatial state-of-the-art machine learning (ML) models, reducing barriers for these types of use cases. The solution is then able to make predictions on the rest of the training data, and route lower-confidence results for human review.
The MLOps Blog
MARCH 12, 2024
Online Inference with Kubernetes using OpenLLM: To handle real-time interactions, deploy your LLM in a Kubernetes cluster with BentoML’s OpenLLM , using it to manage containerized applications for high availability. Caption : RAG system architecture.
AWS Machine Learning Blog
APRIL 2, 2025
At its core, Ray offers a unified programming model that allows developers to seamlessly scale their applications from a single machine to a distributed cluster. A Ray cluster consists of a single head node and a number of connected worker nodes. Ray clusters and Kubernetes clusters pair well together.
AWS Machine Learning Blog
APRIL 10, 2025
As cluster sizes grow, the likelihood of failure increases due to the number of hardware components involved. Each hardware failure can result in wasted GPU hours and requires valuable engineering time to identify and resolve the issue, making the system prone to downtime that can disrupt progress and delay completion.
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