Amazon SageMaker offers a fully managed environment for model inference, letting teams concentrate on building intelligent applications while AWS handles scaling, security, and monitoring.
The service supports real‑time endpoints, batch transform jobs, and inference pipelines that chain preprocessing, model, and post‑processing containers. Advanced optimizations such as quantization, speculative decoding, and specialized hardware like Inf1 Inferentia or GPU G4dn instances dramatically improve throughput and reduce costs.
Azure AI inference unifies access to a growing catalog of foundational models, enabling developers to embed advanced AI into applications with minimal friction. The platform offers a consistent REST API, SDKs for multiple languages, and hardware‑accelerated inference options.
Key capabilities include: - Unified endpoint for chat, vision, and generative models. - SDKs in Python, .NET, and Java for rapid prototyping. - Scalable infrastructure with optional Maia 200 accelerators to cut latency and cost.
Looking ahead, Azure AI is expanding its model catalog, integrating more open‑source LLMs, and enhancing orchestration tools for multi‑agent workflows. Teams can expect tighter integration with Azure OpenAI and broader support for edge deployments.
Vertex AI streamlines the journey from model training to real‑time inference, offering both online and batch prediction endpoints.
Tip: Use the Vertex AI Prediction Dedicated Endpoints for high‑volume, low‑latency workloads, especially with generative models.
SageMaker AI provides a broad selection of ML infrastructure and model deployment options to help meet all your ML inference needs. With SageMaker AI Inference, you can <strong>scale your model deployment, manage models more effectively in production, and reduce operational burden</strong>.
The SageMaker Inference Toolkit implements a model serving stack and can be easily added to any Docker container, making it deployable to SageMaker. This library's serving stack is built on Multi Model Server, and it can serve your own models or those you trained on SageMaker using machine learning frameworks with native SageMaker support.
You can select a model optimization technique such as Speculative Decoding, Quantization and Compilation or combine several techniques, apply them to your models, run benchmark to evaluate the impact of the techniques on output quality and inference performance, and deploy models in just a few clicks. ... Amazon SageMaker AI offers more than 70 instance types with varying levels of compute and memory, including Amazon EC2 Inf1 instances based on AWS Inferentia, high-performance ML inference chips designed and built by AWS, and GPU instances such as Amazon EC2 G4dn.
<strong>An inference pipeline is a Amazon SageMaker AI model that is composed of a linear sequence of two to fifteen containers that process requests for inferences on data</strong>. You use an inference pipeline to define and deploy any combination of pretrained SageMaker AI built-in algorithms and your own ...
Content type options for Amazon SageMaker AI algorithm inference requests include: text/csv, application/json, and application/x-recordio-protobuf. Algorithms that don't support all of these types can support other types.
You only have to bring your raw model artifacts and any dependencies in a requirements.txt file, and SageMaker AI can provide default inference code for you (or you can override the default code with your own custom inference code). SageMaker AI supports this option for the following frameworks: PyTorch, XGBoost. In addition to bringing your model, your AWS Identity and Access Management (IAM) role, and a Docker container (or desired framework and version for which SageMaker AI has a pre-built container), you must also grant permissions to create and deploy models through SageMaker AI Studio.
I want to directly get inferences on my website. How can I use the deployed model for predictions? ... Sagemaker endpoints are not publicly exposed to the Internet. So, you'll need some way of creating a public HTTP endpoint that can route requests to your Sagemaker endpoint. One way you can do this is with an AWS Lambda function fronted by API gateway.
role (str) – An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs that create Amazon SageMaker endpoints use this role to access training data and model artifacts. After the endpoint is created, the inference code might use the IAM role if it needs to access ...
aws sagemaker create-model --model-name "MY-MODEL" --primary-container "$(cat << EOF { "Image": "MY-IMG" } EOF )" --execution-role-arn "MY-EXECUTION-ROL" \
DocumentationAmazon SageMaker Unified StudioUser Guide · Create an endpoint and deploy a modelView your endpoints · Endpoint are locations where you send inference requests to your deployed machine learning models. After you create an endpoint, you can add models to it, test it, and change its settings as needed.
The endpoint URL of your model, in the form https://<your-resouce-name>.openai.azure.com/openai/deployments/<your-deployment-name>, where your-resource-name is your globally unique AOAI resource name, and your-deployment-name is your AI Model deployment name. Depending on your authentication preference, you either need an API key to authenticate against the service, or Entra ID credentials. An api-version. Latest preview or GA version listed in the Data plane - inference row in the API Specs table.
The Azure AI model inference is <strong>an API that exposes a common set of capabilities for foundational models and that can be used by developers to consume predictions from a diverse set of models in a uniform and consistent way</strong>.
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