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With the SDK, you can train and deploy models using popular deep learning frameworks, algorithms provided by Amazon, or your own algorithms built into SageMaker-compatible Docker images. Here you’ll find an overview and API documentation for SageMaker Python SDK.

model_card (ModeCard or ModelPackageModelCard) – document contains qualitative and quantitative information about a model (default: None). model_life_cycle (ModelLifeCycle) – ModelLifeCycle object (default: None). validation_specification (str | PipelineVariable | None) – ... A sagemaker.model.ModelPackage instance or pipeline step arguments in case the Model instance is built with PipelineSession

After training is complete, calling deploy() creates a hosted SageMaker endpoint and returns an SKLearnPredictor instance that can be used to perform inference against the hosted model. Technical documentation on preparing Scikit-learn scripts for SageMaker training and using the Scikit-learn ...

April 1, 2025 - In the meantime, you could consider using the AWS Management Console for setting up SageMaker Unified Studio, as the setup process involves several steps including configuring AWS IAM Identity Center, setting up necessary VPC and IAM roles, creating a SageMaker domain, and enabling certain features like Amazon Q Developer Pro. As the service matures and moves out of preview, it's likely that more comprehensive documentation and support for infrastructure-as-code solutions will become available.

June 19, 2024 - This comprehensive tutorial teaches you how to use AWS SageMaker to build, train, and deploy machine learning models.

1 week ago - Accelerate AI in SageMaker with a comprehensive set of AI development capabilities that are secure by design. Train, customize, and deploy ML and foundation models (FMs) on a highly performant and cost-effective infrastructure.

1 week ago - Using SageMaker Data Wrangler’s data selection tool, you can choose the data you want from various data sources and import it easily. SageMaker Data Wrangler contains over 300 built-in data transformations so you can quickly normalize, transform, and combine features without having to write any code.

Strands Agents is a simple-to-use, code-first framework for building agents · First, install the Strands Agents SDK:

See SageMaker V2 Examples for V2 documentation and examples.

sagemaker/<your-endpoint-name>, with AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and AWS_REGION_NAME · azure/<your_deployment_name>, with AZURE_API_KEY, AZURE_API_BASE, AZURE_API_VERSION, and the optional AZURE_AD_TOKEN and AZURE_API_TYPE · If your provider offers an OpenAI-compatible endpoint, just add an openai/ prefix to your full model name.

Best Practices for Evaluating Fairness and Explainability in the ML Lifecycle Sample Notebooks Guide to the SageMaker Clarify Documentation Train a Model with Amazon SageMaker Deploy a Model in Amazon SageMaker Deploy a Model on SageMaker Hosting Services Best Practices for Deploying Models on SageMaker Hosting Services Get Inferences for an Entire Dataset with Batch Transform Validate a Machine Learning Model Monitoring a Model in Production Use Machine Learning Frameworks, Python, and R with Amazon SageMaker Use Apache MXNet with Amazon SageMaker What do you want to do?

1 week ago - Access a rich repository of resources such as SDK, documentation, and API reference to help you get started with Amazon SageMaker AI and help you build, train, and deploy ML models quickly and easily.

23.2. Using Amazon SageMaker · 23.3. Using AWS EC2 Instances · 23.4. Using Google Colab · 23.5. Selecting Servers and GPUs · 23.6. Contributing to This Book · 23.7. Utility Functions and Classes · 23.8. The d2l API Document · References ·

This section explains how to integrate your Amazon SageMaker installation to work with lakeFS.

1 week ago - You are viewing the latest developer preview docs. Click here to view docs for the latest stable release · vLLM supports the following hardware platforms:

Information about getting started with Amazon SageMaker Unified Studio. You need to gain access, then create or join a project. As part of this process you might also need to configure credentials for IAM or SSO access. A sample notebook is also available to help you get started.

Amazon SageMaker supports using Amazon Elastic File System (EFS) and FSx for Lustre as data sources to use during training. If you want use those data sources, create a file system (EFS/FSx) and mount the file system on an Amazon EC2 instance. For more information about setting up EFS and FSx, see the following documentation...

The simple guide will show you how to use the 'Create model' operation which builds a docker image with your relevant model, packaged in such a way that the Sagemaker API will accept it. Before trying to use the 'Create model' operation it is advised you read the relevant documentation on AWS ...