# What is Amazon SageMaker AI?
Amazon SageMaker AI is a fully managed machine learning (ML) service. With SageMaker AI, data scientists and developers can quickly and confidently build, train, and deploy ML models into a production-ready hosted environment. It provides a UI experience for running ML workflows that makes SageMaker AI ML tools available across multiple integrated development environments (IDEs).
With SageMaker AI, you can store and share your data without having to build and manage your own servers. This gives you or your organizations more time to collaboratively build and develop your ML workflow, and do it sooner. SageMaker AI provides managed ML algorithms to run efficiently against extremely large data in a distributed environment. With built-in support for bring-your-own-algorithms and frameworks, SageMaker AI offers flexible distributed training options that adjust to your specific workflows. Within a few steps, you can deploy a model into a secure and scalable environment from the SageMaker AI console.
**Topics**
+ [
## Amazon SageMaker AI rename
](#whatis-rename)
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## Amazon SageMaker and Amazon SageMaker AI
](#whatis-rename-unified)
+ [
## Pricing for Amazon SageMaker AI
](#whatis-pricing)
+ [
# Recommendations for a first-time user of Amazon SageMaker AI
](first-time-user.md)
+ [
# Overview of machine learning with Amazon SageMaker AI
](how-it-works-mlconcepts.md)
+ [
# Amazon SageMaker AI Features
](whatis-features.md)
## Amazon SageMaker AI rename
On December 03, 2024, Amazon SageMaker was renamed to Amazon SageMaker AI. This name change does not apply to any of the existing Amazon SageMaker features.
### Legacy namespaces remain the same
The `sagemaker` API namespaces, along with the following related namespaces, remain unchanged for backward compatibility purposes.
+ AWS CLI commands
+ [Managed policies](https://docs.aws.amazon.com/sagemaker/latest/dg/security-iam-awsmanpol.html) containing `AmazonSageMaker` prefixes
+ [Service endpoints](https://docs.aws.amazon.com/general/latest/gr/sagemaker.html) containing `sagemaker`
+ [AWS CloudFormation](https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/AWS_SageMaker.html) resources containing `AWS::SageMaker` prefixes
+ Service-linked role containing `AWSServiceRoleForSageMaker`
+ Console URLs containing `sagemaker`
+ Documentation URLs containing `sagemaker`
## Amazon SageMaker and Amazon SageMaker AI
On December 03, 2024, Amazon released the next generation of Amazon SageMaker.
Amazon SageMaker is a unified platform for data, analytics, and AI. Bringing together AWS machine learning and analytics capabilities, the next generation of SageMaker delivers an integrated experience for analytics and AI with unified access to all your data.
Amazon SageMaker includes the following capabilities:
+ Amazon SageMaker AI (formerly Amazon SageMaker) - Build, train, and deploy ML and foundation models, with fully managed infrastructure, tools, and workflows
+ Amazon SageMaker Lakehouse – Unify data access across Amazon S3 data lakes, Amazon Redshift, and other data sources
+ Amazon SageMaker Data and AI Governance – Discover, govern, and collaborate on data and AI securely with Amazon SageMaker Catalog, built on Amazon DataZone
+ SQL Analytics - Gain insights with the most price-performant SQL engine with Amazon Redshift
+ Amazon SageMaker Data Processing - Analyze, prepare, and integrate data for analytics and AI using open-source frameworks on Amazon Athena, Amazon EMR, and AWS Glue
+ Amazon SageMaker Unified Studio – Build with all your data and tools for analytics and AI in a single development environment
+ Amazon Bedrock - Build and scale generative AI applications
For more information, refer to [Amazon SageMaker](https://aws.amazon.com/sagemaker).
## Pricing for Amazon SageMaker AI
For information about [AWS Free Tier](https://aws.amazon.com/free) limits and the cost of using SageMaker AI, see [Amazon SageMaker AI Pricing](https://aws.amazon.com/sagemaker/pricing/).
# Recommendations for a first-time user of Amazon SageMaker AI
If you're a first-time user of SageMaker AI, we recommend that you complete the following:
1. **[Overview of machine learning with Amazon SageMaker AI](how-it-works-mlconcepts.md)** – Get an overview of the machine learning (ML) lifecycle and learn about solutions that are offered. This page explains key concepts and describes the core components involved in building AI solutions with SageMaker AI.
1. **[Guide to getting set up with Amazon SageMaker AI](gs.md)** – Learn how to set up and use SageMaker AI based on your needs.
1. **[Automated ML, no-code, or low-code](use-auto-ml.md)** – Learn about low-code and no-code ML options that simplify a ML workflow by automating machine learning tasks. These options are helpful ML learning tools because they provide visibility into the code by generating notebooks for each of the automated ML tasks.
1. **[Machine learning environments offered by Amazon SageMaker AI](machine-learning-environments.md)** – Familiarize yourself with the ML environments that you can use to develop your ML workflow, such as information and examples about ready-to-use and custom models.
1. **Explore other topics** – Use the SageMaker AI Developer Guide's table of contents to explore more topics. For example, you can find information about ML lifecycle stages, in [Overview of machine learning with Amazon SageMaker AI](how-it-works-mlconcepts.md), and various solutions that SageMaker AI offers.
1. **[Amazon SageMaker AI resources](https://aws.amazon.com/sagemaker/resources)** – Refer to the various developer resources that SageMaker AI offers.
# Overview of machine learning with Amazon SageMaker AI
This section describes a typical machine learning (ML) workflow and describes how to accomplish those tasks with Amazon SageMaker AI.
In machine learning, you *teach* a computer to make predictions or inferences. First, you use an algorithm and example data to train a model. Then, you integrate your model into your application to generate inferences in real time and at scale.
The following diagram shows the typical workflow for creating an ML model. It includes three stages in a circular flow that we cover in more detail proceeding the diagram:
+ Generate example data
+ Train a model
+ Deploy the model
![\[The three stages of ML model creation.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/ml-concepts-10.png)
The diagram shows how to perform the following tasks in most typical scenarios:
1. **Generate example data** – To train a model, you need example data. The type of data that you need depends on the business problem that you want the model to solve. This relates to the inferences that you want the model to generate. For example, if you want to create a model that predicts a number from an input image of a handwritten digit. To train this model, you need example images of handwritten numbers.
Data scientists often devote time exploring and preprocessing example data before using it for model training. To preprocess data, you typically do the following:
1. **Fetch the data** – You might have in-house example data repositories, or you might use datasets that are publicly available. Typically, you pull the dataset or datasets into a single repository.
1. **Clean the data** – To improve model training, inspect the data and clean it, as needed. For example, if your data has a `country name` attribute with values `United States` and `US`, you can edit the data to be consistent.
1. **Prepare or transform the data** – To improve performance, you might perform additional data transformations. For example, you might choose to combine attributes for a model that predicts the conditions that require de-icing an aircraft. Instead of using temperature and humidity attributes separately, you can combine those attributes into a new attribute to get a better model.
In SageMaker AI, you can preprocess example data using [SageMaker APIs](https://docs.aws.amazon.com/sagemaker/latest/APIReference/Welcome.html) with the [SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/) in an integrated development environment (IDE). With SDK for Python (Boto3) you can fetch, explore, and prepare your data for model training. For information about data preparation, processing, and transforming your data, see [Recommendations for choosing the right data preparation tool in SageMaker AI](data-prep.md), [Data transformation workloads with SageMaker Processing](processing-job.md), and [Create, store, and share features with Feature Store](feature-store.md).
1. **Train a model** – Model training includes both training and evaluating the model, as follows:
+ **Training the model** – To train a model, you need an algorithm or a pre-trained base model. The algorithm you choose depends on a number of factors. For a built-in solution, you can use one of the algorithms that SageMaker provides. For a list of algorithms provided by SageMaker and related considerations, see [Built-in algorithms and pretrained models in Amazon SageMaker](algos.md). For a UI-based training solution that provides algorithms and models, see [SageMaker JumpStart pretrained models](studio-jumpstart.md).
You also need compute resources for training. Your resource use depends on the size of your training dataset and how quickly you need the results. You can use resources ranging from a single general-purpose instance to a distributed cluster of GPU instances. For more information, see [Train a Model with Amazon SageMaker](how-it-works-training.md).
+ **Evaluating the model** – After you train your model, you evaluate it to determine whether the accuracy of the inferences is acceptable. To train and evaluate your model, use the [SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/) to send requests to the model for inferences through one of the available IDEs. For more information about evaluating your model, see [Data and model quality monitoring with Amazon SageMaker Model Monitor](model-monitor.md).
1. **Deploy the model** – You traditionally re-engineer a model before you integrate it with your application and deploy it. With SageMaker AI hosting services, you can deploy your model independently, which decouples it from your application code. For more information, see [Deploy models for inference](deploy-model.md).
Machine learning is a continuous cycle. After deploying a model, you monitor the inferences, collect more high-quality data, and evaluate the model to identify drift. You then increase the accuracy of your inferences by updating your training data to include the newly collected high-quality data. As more example data becomes available, you continue retraining your model to increase accuracy.
# Amazon SageMaker AI Features
Amazon SageMaker AI includes the following features.
**Topics**
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## New features for re:Invent 2024
](#whatis-features-alpha-new)
+ [
## Machine learning environments
](#whatis-features-alpha-mle)
+ [
## Major features
](#whatis-features-alpha-major)
## New features for re:Invent 2024
SageMaker AI includes the following new features for re:Invent 2024.
**[HyperPod recipes](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-hyperpod-recipes.html) **
You can run recipes within Amazon SageMaker HyperPod or as SageMaker training jobs. You use the HyperPod training adapter as the framework to help you run end-to-end training workflows. The training adapter is built on the NVIDIA NeMo framework and Neuronx Distributed Training package.
**[HyperPod in Studio](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-hyperpod-studio.html) **
In Amazon SageMaker Studio, you can launch machine learning workloads on HyperPod clusters and view HyperPod cluster information. The increased visibility into cluster details and hardware metrics can help your team identify the right candidate for your pre-training or fine-tuning workloads.
**[HyperPod task governance](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-hyperpod-eks-operate-console-ui-governance.html) **
Amazon SageMaker HyperPod task governance is a robust management system designed to streamline resource allocation and ensure efficient utilization of compute resources across teams and projects for your Amazon EKS clusters. HyperPod task governance also provides Amazon EKS cluster Observability, offering real-time visibility into cluster capacity, compute availability and usage, team allocation and utilization, and task run and wait time information.
**[Amazon SageMaker Partner AI Apps](https://docs.aws.amazon.com/sagemaker/latest/dg/partner-apps.html) **
With Amazon SageMaker Partner AI Apps, users get access to generative artificial intelligence (AI) and machine learning (ML) development applications built, published, and distributed by industry-leading application providers. Partner AI Apps are certified to run on SageMaker AI. With Partner AI Apps, users can accelerate and improve how they build solutions based on foundation models (FM) and classic ML models without compromising the security of their sensitive data, which stays completely within their trusted security configuration and is never shared with a third party.
**[Q Developer is available in Canvas](https://docs.aws.amazon.com/sagemaker/latest/dg/canvas-q.html) **
You can chat with Amazon Q Developer in Amazon SageMaker Canvas using natural language for generative AI assistance with solving your machine learning problems. You can converse with Q Developer to discuss the steps of a machine learning workflow and leverage Canvas functionality such as data transforms, model building, and deployment.
**[SageMaker training plans](https://docs.aws.amazon.com/sagemaker/latest/dg/reserve-capacity-with-training-plans.html) **
Amazon SageMaker training plans are a compute reservation capability designed for large-scale AI model training workloads running on SageMaker training jobs and HyperPod clusters. They provide predictable access to high-demand GPU-accelerated computing resources within specified timelines. You can specify a desired timeline, duration, and maximum compute resources, and SageMaker training plans automatically manages infrastructure setup, workload execution, and fault recovery. This allows for efficiently planning and executing mission-critical AI projects with a predictable cost model.
## Machine learning environments
SageMaker AI includes the following machine learning environments.
**[SageMaker Canvas](canvas.md)**
An auto ML service that gives people with no coding experience the ability to build models and make predictions with them.
**[Code Editor](https://docs.aws.amazon.com/sagemaker/latest/dg/code-editor.html) **
Code Editor extends Studio so that you can write, test, debug and run your analytics and machine learning code in an environment based on Visual Studio Code - Open Source ("Code-OSS").
**[SageMaker geospatial capabilities](geospatial.md)**
Build, train, and deploy ML models using geospatial data.
**[SageMaker HyperPod](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-hyperpod.html) **
Amazon SageMaker HyperPod is a capability of SageMaker AI that provides an always-on machine learning environment on resilient clusters that you can run any machine learning workloads for developing large machine learning models such as large language models (LLMs) and diffusion models.
**[JupyterLab in Studio](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-updated-jl.html) **
JupyterLab in Studio improves latency and reliability for Studio Notebooks
**[Studio](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-updated.html) **
Studio is the latest web-based experience for running ML workflows. Studio offers a suite of IDEs, including Code Editor, a new Jupyterlab application, RStudio, and Studio Classic.
**[Amazon SageMaker Studio Classic](studio.md)**
An integrated machine learning environment where you can build, train, deploy, and analyze your models all in the same application.
**[SageMaker Studio Lab](studio-lab.md)**
A free service that gives customers access to AWS compute resources in an environment based on open-source JupyterLab.
**[RStudio on Amazon SageMaker AI](rstudio.md)**
An integrated development environment for R, with a console, syntax-highlighting editor that supports direct code execution, and tools for plotting, history, debugging and workspace management.
## Major features
SageMaker AI includes the following major features in alphabetical order excluding any SageMaker AI prefix.
**[Amazon Augmented AI](a2i-use-augmented-ai-a2i-human-review-loops.md)**
Build the workflows required for human review of ML predictions. Amazon A2I brings human review to all developers, removing the undifferentiated heavy lifting associated with building human review systems or managing large numbers of human reviewers.
**[AutoML step](build-and-manage-steps.md)**
Create an AutoML job to automatically train a model in Pipelines.
**[SageMaker Autopilot](autopilot-automate-model-development.md)**
Users without machine learning knowledge can quickly build classification and regression models.
**[Batch Transform](batch-transform.md)**
Preprocess datasets, run inference when you don't need a persistent endpoint, and associate input records with inferences to assist the interpretation of results.
**[SageMaker Clarify](clarify-configure-processing-jobs.md#clarify-fairness-and-explainability)**
Improve your machine learning models by detecting potential bias and help explain the predictions that models make.
**[Collaboration with shared spaces](domain-space.md)**
A shared space consists of a shared JupyterServer application and a shared directory. All user profiles in a Amazon SageMaker AI domain have access to all shared spaces in the domain.
**[SageMaker Data Wrangler](data-wrangler.md)**
Import, analyze, prepare, and featurize data in SageMaker Studio. You can integrate Data Wrangler into your machine learning workflows to simplify and streamline data pre-processing and feature engineering using little to no coding. You can also add your own Python scripts and transformations to customize your data prep workflow.
**[Data Wrangler data preparation widget](data-wrangler-interactively-prepare-data-notebook.md)**
Interact with your data, get visualizations, explore actionable insights, and fix data quality issues.
**[SageMaker Debugger](train-debugger.md)**
Inspect training parameters and data throughout the training process. Automatically detect and alert users to commonly occurring errors such as parameter values getting too large or small.
**[SageMaker Edge Manager](edge.md)**
Optimize custom models for edge devices, create and manage fleets and run models with an efficient runtime.
**[SageMaker Experiments](experiments.md)**
Experiment management and tracking. You can use the tracked data to reconstruct an experiment, incrementally build on experiments conducted by peers, and trace model lineage for compliance and audit verifications.
**[SageMaker Feature Store](feature-store.md)**
A centralized store for features and associated metadata so features can be easily discovered and reused. You can create two types of stores, an Online or Offline store. The Online Store can be used for low latency, real-time inference use cases and the Offline Store can be used for training and batch inference.
**[SageMaker Ground Truth](sms.md)**
High-quality training datasets by using workers along with machine learning to create labeled datasets.
**[SageMaker Ground Truth Plus](gtp.md)**
A turnkey data labeling feature to create high-quality training datasets without having to build labeling applications and manage the labeling workforce on your own.
**[SageMaker Inference Recommender](inference-recommender.md)**
Get recommendations on inference instance types and configurations (e.g. instance count, container parameters and model optimizations) to use your ML models and workloads.
**[Inference shadow tests](shadow-tests.md)**
Evaluate any changes to your model-serving infrastructure by comparing its performance against the currently deployed infrastructure.
**[SageMaker JumpStart](studio-jumpstart.md)**
Learn about SageMaker AI features and capabilities through curated 1-click solutions, example notebooks, and pretrained models that you can deploy. You can also fine-tune the models and deploy them.
**[SageMaker ML Lineage Tracking](lineage-tracking.md)**
Track the lineage of machine learning workflows.
**[SageMaker Model Building Pipelines](pipelines.md)**
Create and manage machine learning pipelines integrated directly with SageMaker AI jobs.
**[SageMaker Model Cards](model-cards.md)**
Document information about your ML models in a single place for streamlined governance and reporting throughout the ML lifecycle.
**[SageMaker Model Dashboard](model-dashboard.md)**
A pre-built, visual overview of all the models in your account. Model Dashboard integrates information from SageMaker Model Monitor, transform jobs, endpoints, lineage tracking, and CloudWatch so you can access high-level model information and track model performance in one unified view.
**[SageMaker Model Monitor](model-monitor.md)**
Monitor and analyze models in production (endpoints) to detect data drift and deviations in model quality.
**[SageMaker Model Registry](model-registry.md)**
Versioning, artifact and lineage tracking, approval workflow, and cross account support for deployment of your machine learning models.
**[SageMaker Neo](neo.md)**
Train machine learning models once, then run anywhere in the cloud and at the edge.
**[Notebook-based Workflows](notebook-auto-run.md)**
Run your SageMaker Studio notebook as a non-interactive, scheduled job.
**[Preprocessing](processing-job.md)**
Analyze and preprocess data, tackle feature engineering, and evaluate models.
**[SageMaker Projects](sagemaker-projects.md)**
Create end-to-end ML solutions with CI/CD by using SageMaker Projects.
**[Reinforcement Learning](reinforcement-learning.md)**
Maximize the long-term reward that an agent receives as a result of its actions.
**[SageMaker Role Manager](role-manager.md)**
Administrators can define least-privilege permissions for common ML activities using custom and preconfigured persona-based IAM roles.
**[SageMaker Serverless Endpoints](serverless-endpoints.md)**
A serverless endpoint option for hosting your ML model. Automatically scales in capacity to serve your endpoint traffic. Removes the need to select instance types or manage scaling policies on an endpoint.
**[Studio Classic Git extension](studio-git-attach.md)**
A Git extension to enter the URL of a Git repository, clone it into your environment, push changes, and view commit history.
**[SageMaker Studio Notebooks](notebooks.md)**
The next generation of SageMaker notebooks that include AWS IAM Identity Center (IAM Identity Center) integration, fast start-up times, and single-click sharing.
**[SageMaker Studio Notebooks and Amazon EMR](studio-notebooks-emr-cluster.md)**
Easily discover, connect to, create, terminate and manage Amazon EMR clusters in single account and cross account configurations directly from SageMaker Studio.
**[SageMaker Training Compiler](training-compiler.md)**
Train deep learning models faster on scalable GPU instances managed by SageMaker AI.