Amazon SageMaker is huge, so you can understand why it has so many core features to offer. It is the most favored tool by data scientists and developers because they can quickly and easily develop and train machine learning models since it is a fully managed machine learning service. These models can directly be deployed into a production-ready hosted environment.
This article will speak to us about AWS SageMaker in detail and walk us through different concepts to help us gain insights into the subject better. Also you can learn AWS Sagemaker for free after reading this article and start your journey in this domain.
Table of Contents
Describing AWS SageMaker
Amazon SageMaker is a cloud machine-learning environment that allows developers to develop, train, and deploy machine learning (ML) models in the cloud. It also allows developers to deploy ML models on embedded and edge devices. You do not have to manage servers since it provides an integrated Jupyter authoring notebook instance to easily access your data sources for exploration and analysis. It offers common machine learning algorithms optimized to run effectively and efficiently against extremely large data in a distributed environment.
SageMaker gives you native support to bring-your-own-algorithms and frameworks by offering flexible distributed training choices adjusting to your workflow specifics. You can deploy a model into a secure and scalable environment by launching it by clicking a few times from SageMaker Studio or the SageMaker console. With no minimum fees and no prior commitments, you can bill training and hosting by minutes of usage.
Amazon SageMaker Features
Amazon SageMaker’s features make it unique and attractive. They include:
- SageMaker Studio: It is an integrated machine learning platform that allows you to build, train, deploy and analyze all your models in the same application.
- SageMaker Canvas: It is an auto-machine learning service that provides people with no coding experience the ability to create models and predict through them.
- SageMaker Ground Truth Plus: It is a turnkey data labeling feature that builds high-quality training datasets without developing labeling applications and managing the labeling workforce on your own.
- SageMaker Studio Lab: It is a free Amazon service that provides customers access to AWS compute resources in an environment based on open-source JupyterLab.
- SageMaker Training Compiler: It is a feature that trains deep learning models faster on scalable GPU instances managed by SageMaker.
- SageMaker Studio Universal Notebook: It serves as a one-stop to quickly discover, create, connect to, terminate, and manage Amazon EMR clusters in a single account and cross-account configurations from SageMaker Studio.
- SageMaker Serverless Endpoints: It serves as a serverless endpoint choice to host your machine learning model. It automatically scales up the capacity to serve your endpoint traffic. It also removes the necessity to select insurance types or manage scaling policies on an endpoint.
- SageMaker Inference Recommender: you can get recommendations for inference instance types and configurations, for example, instance count, model optimizations, and container parameters, to use your machine learning models and workloads.
- SageMaker Model Registry: You will get support for deploying your machine learning models through versioning, artifact and lineage tracking, cross-account, and approval workflow.
- SageMaker Projects: This feature allows you to develop end-to-end machine learning solutions with CI/CD.
- SageMaker Model Building Pipelines: The feature allows you to develop and manage machine learning pipelines integrated directly with SageMaker jobs.
- SageMaker ML Lineage Tracking: This feature is used to track the machine learning workflow lineage.
- SageMaker Data Wrangler: This feature imports, prepares, analyzes, and featurize data in SageMaker Studio. You can integrate your machine learning workflows with Data Wrangler to simplify and streamline data preprocessing and feature engineering using very little or no coding at all. You can add your own Python scripts and transformations if you want to customize your data preparation workflow.
- SageMaker feature Store: It is a central repository for features and its associated metadata for features to be easily discovered and reused. You can develop two kinds of stores, and they are: Online and Offline stores. The Online stores are used to work with low latency and real-time inference use cases, while the Offline stores are used to train and batch inferences.
- SageMaker JumpStart: You can learn about SageMaker’s features and capabilities through curated one-click solutions, example notebooks, and pre-trained models that you deploy and also deploy the models after finetuning them.
- SageMaker Clarify: It boasts your machine learning models by detecting solid bias and helping explain the predictions that models make.
- SageMaker Edge Manager: It optimizes the custom models for edge devices and develops and manages fleet and run models with efficient runtime.
- SageMaker Ground Truth: They are the high-quality datasets that use workers along with machine learning to create labeled datasets.
- Amazon Augmented AI: This allows you to develop the workflows needed for the human review of machine learning predictions. Amazon A2I provides human reviews to all developers by removing all the undifferentiated heavy lifting tied up with developing human review systems and managing a huge number of human reviews.
- SageMaker Studio Notebooks: It is the future version of SageMaker notebooks, including AWS single sign-on (AWS SSO) integration, single-cluck sharing and fast start-up times.
- SageMaker Experiments: It allows you to experiment with management and tracking approaches. This tracked data can be used to reconstruct an experiment that is incrementally built on experiments conducted by the other fellow mates and trace model lineage for compliance and audit verifications.
- SageMaker Debugger: It inspects training parameters and data all through the training process. It automatically detects and alerts users about commonly occurring errors like parameter values becoming too big or small.
- SageMaker Autopilot: This helps users quickly develop classification and regression models with no machine learning knowledge.
- SageMaker Model Monitor: It monitors and analyzes models in production endpoints to detect data drift and deviations in model quality.
- SageMaker Neo: It trains the machine learning models only once and runs it anywhere in the cloud and at the edge.
- SageMaker Elastic Inference: It increases the pace of the throughput and decreases the latency of getting real-time inferences.
- Reinforcement Learning: Reinforcement does maximize the long-term reward that an agent gets as a result of the actions.
- Preprocessing: The process analyzes and preprocesses the data, evaluates models and tackles feature engineering.
- Batch Transform: It runs inferences and preprocesses datasets when you don’t require a persistent endpoint and associates the inferences with the input records to assist in interpreting results.
Are You a First-Timer in Using AWS SageMaker?
If you are using SageMaker for the first time, we suggest you to:
- Understand how AWS SageMaker works. It is important to learn and understand SageMaker’s key concepts and core components in developing AI solutions with SageMaker.
- Describe your SageMakaer Prerequisites. This will help you understand and train you to set up your AWS account.
- Amazon Sagemaker Autopilot simplifies ML experience. It gives you experience by automating machine learning tasks. It provides you with the easiest ways to learn if you are a beginner. It is an excellent machine learning tool providing visibility into the programs, with notebooks developed for every automated machine learning task. You can build, train, and deploy machine learning models through autopilots. You can learn AWS SageMaker by enrolling in the best course available.
- You can submit Python codes to train deep learning frameworks. You can choose to use your own training scripts to train the models. You can use SageMaker to train and deploy your own custom algorithms directly with Docker.
Unlike other AWS products, there are no limitations to using AWS SageMAker. It is one of the interesting tools to work with as it is a fully managed service enabling the users to quickly and easily integrate machine learning-based models into the applications. We have seen various features offered by AWS SageMaker in this article, and you have also learned how it is integrated with machine learning concepts.
We recommend you to learn AWS SageMaker if you are a beginner. If you are willing to explore more about cloud computing and services, you can register for a Cloud Computing course online and enrich your knowledge in the domain.