AWS Sagemaker

                                                                          Amazon SageMaker

Amazon SageMaker is a machine learning service that Amazon wholly manages. Data scientists and developers can use SageMaker to construct and train machine learning models fast and easily, then deploy them directly into a production-ready hosted environment. You don't have to manage servers because it has an integrated Jupyter writing notebook instance for easy access to your data sources for exploration and analysis. It also includes common machine learning methods that have been improved for use in a distributed setting with exceptionally huge data sets. SageMaker offers versatile distributed training alternatives that adapt to your individual workflows thanks to native support for bring-your-own-algorithms and frameworks. Launch a model from SageMaker Studio or the SageMaker console in a safe and scalable environment with just a few clicks. Training and hosting are invoiced by the minute, with no minimum payments or commitments up ahead.
Capabilities

When training and deploying machine learning models, SageMaker allows developers to work at several levels of abstraction. SageMaker delivers pre-trained ML models that may be deployed as-is at the greatest degree of abstraction. SageMaker also has a variety of built-in machine learning algorithms that developers may train on their data. SageMaker also offers managed TensorFlow and Apache MXNet instances, allowing developers to build their machine learning algorithms from the ground up. A developer can connect their SageMaker-enabled ML models to other AWS services, such as the Amazon DynamoDB database for structured data storage, AWS Batch for offline batch processing, or Amazon Kinesis for real-time processing, regardless of the level of abstraction employed.

Amazon SageMaker Features
SageMaker Studio is a software that allows you to create your own
An integrated machine learning environment allows you to create, train, deploy and analyze your models all in one place.
SageMaker Canvas is a program that allows you to create your own It is an automatic machine learning tool that allows anyone with no coding skills to create models and make predictions.
Ground Truth Plus by SageMaker
Create high-quality training datasets without having to construct labeling applications or manage your own labeling workforce with our turnkey data labeling capability.
SageMaker Studio Lab is a software development studio.
A free service that allows clients to use AWS computational resources in an open-source JupyterLab environment.

Compiler for SageMaker Training

SageMaker's scalable GPU instances allow you to train deep learning models faster.

Feature Store for SageMaker
It is a centralized repository for features and metadata, allowing them to be readily discovered and reused. You have the option of creating an online or offline store. The Offline Store can be utilized for training and batch inference, while the Online Store can be used for low latency, real-time inference.
SageMaker JumpStart is a program that helps you get started with SageMaker.  Use curated 1-click solutions, example notebooks, and pre-trained models to learn about SageMaker features and capabilities. You may also fine-tune and deploy the model.

Clarify with SageMaker
Improve the accuracy of your machine learning models by detecting potential bias and explaining the models' predictions.
SageMaker Edge Manager is a program that allows you to manage the edges of Create and manage fleets and execute models with a short runtime by optimizing bespoke models for edge devices.

Ground Truth by Sagemaker
We used workers and machine learning to create high-quality training datasets to produce labeled datasets.
Some other features of the sage maker are given below:

• SageMaker Studio Universal Notebook

• SageMaker Serverless Endpoints

• SageMaker Inference Recommender

• SageMaker Model Registry

• SageMaker Projects

• SageMaker Model Building Pipelines

• SageMaker ML Lineage Tracking

• SageMaker Data Wrangler

• Amazon Augmented AI

• SageMaker Studio Notebooks

• SageMaker Experiments

• SageMaker Debugger

• SageMaker Autopilot

• SageMaker Model Monitor

• SageMaker Neo

• SageMaker Elastic Inference

• Reinforcement Learning

• Preprocessing

• Batch Transform

Amazon SageMaker Pricing

By bringing together a comprehensive range of features purpose-built for ML, Amazon SageMaker enables data scientists and developers to prepare swiftly, build, train, and deploy high-quality machine learning (ML) models. SageMaker is compatible with the industry's most popular machine learning frameworks, toolkits, and programming languages. You just pay for what you use with SageMaker. You have two payment options: On-Demand Pricing, which has no minimum fees and no upfront commitments, and SageMaker Savings Plans, which has a flexible, use-based pricing plan in exchange for a consistent amount of usage commitment.

How Amazon SageMaker Works

SageMaker is a fully managed service that allows you to integrate machine learning-based models into your applications quickly and effectively. This section discusses how SageMaker works and gives an overview of machine learning. If this is your first time using SageMaker, we recommend that you read the parts below in order:

1. Amazon SageMaker for Machine Learning

2. Investigate, Analyze, and Process Information

3. Use Amazon SageMaker to train a model

4. Use Amazon SageMaker to deploy a model

5. Use Amazon SageMaker with Machine Learning Frameworks, Python, and R.

6. Begin using Amazon SageMaker

Development Interfaces

Developers can interact with SageMaker through a variety of interfaces. The first is a web API for controlling a SageMaker server instance from afar. While the web API is independent of the developer's programming language, Amazon offers SageMaker API bindings for various languages, including Python, JavaScript, Ruby, Java, and Go. SageMaker now supports managed Jupyter Notebook instances for interactive programming of SageMaker and other apps.