Explain the use cases of the AWS SageMaker

This recipe explains what the use cases of the AWS SageMaker

Recipe Objective - Explain the use cases of the AWS SageMaker?

The Amazon SageMaker is a widely used service and is defined as a managed service in the Amazon Web Services (AWS) cloud which provides tools to build, train and deploy machine learning (ML) models for predictive analytics applications. Amazon SageMaker platform automates the unvarying work of building the production-ready artificial intelligence (AI) pipelines. Amazon SageMaker also enables the developers to deploy Machine Learning models on embedded systems and edge devices. The Amazon SageMaker creates the fully managed Machine Learning instance in the Amazon Elastic Compute Cloud (EC2). It supports the open-source Jupyter Notebook web application which enables developers to share live code and collaborate. Amazon SageMaker runs the Jupyter computational processing notebooks. The notebooks include the drivers, packages and libraries for similar deep learning platforms and frameworks. Developers can launch the prebuilt notebook that AWS supplies for a variety of applications and use cases and they can then customize it according to the data set and schema that needs to be further trained. Developers also can use the custom-built algorithms written in one of the supported Machine Learning frameworks or some code that has been packaged as the Docker container image. Amazon SageMaker helps in pulling the data from Amazon Simple Storage Service (S3) and there is no defined practical limit to the size of the data set.

Learn to Build ETL Data Pipelines on AWS

Benefits of Amazon SageMaker

  • The Amazon SageMaker enables more people to innovate with Machine Learning through the choice of tools—integrated development environments for data scientists, machine learning engineers and no-code visual interfaces for the business analysts thus making machine learning more accessible. Amazon SageMaker helps in accessing, labelling, and processing large amounts of structured data (tabular data) and unstructured data (photos, video, and audio) for Machine Learning thus helping in preparing data in scale. Amazon SageMaker helps in reducing the training time from hours to minutes with the optimized infrastructure thereby boosting team productivity up to 10 times with the purpose-built tools thus accelerating machine learning development. Amazon SageMaker helps in automating and standardizing MLOps practices across the organization to build, train, deploy, and manage machine learning models at a larger scale.

System Requirements

  • Any Operating System(Mac, Windows, Linux)

This recipe explains Amazon SageMaker and Use cases of Amazon SageMaker.

Use cases of Amazon SageMaker

    • It provides one-click Jupyter Notebooks

Amazon SageMaker Studio Notebooks help build ML models faster and collaborate with the team. Amazon SageMaker Studio notebooks provide one-click Jupyter notebooks that a user can start working within seconds. Further, the underlying compute resources are fully elastic, so a user can easily dial up or down the available resources and the changes take place automatically in the background without interrupting your work. Amazon SageMaker also enables one-click sharing of notebooks. All the code dependencies are automatically captured, so you can easily collaborate with others and they’ll get the same notebook, saved in the same place.

    • It provides RStudio Interface.

Amazon SageMaker brings the existing RStudio licenses and lift-and-shift the RStudio environments to the Amazon SageMaker easily and securely. RStudio on Amazon SageMaker provides users with a familiar RStudio IDE with on-demand cloud computing resources. Users can launch RStudio with a single click from Amazon SageMaker because it is fully managed, and R developers can dial-up compute from within the same interface reducing interruptions to work and improving productivity.

    • It provides AutoML.

Amazon SageMaker autopilot automatically builds, trains, and tunes the best machine learning models, based on the data while allowing users to maintain full control and visibility. Users then can directly deploy the model to the production with just one click or iterate to improve the model quality.

    • It provides Pre-built solutions for the open-source models

Amazon SageMaker JumpStart helps users to quickly get started with Machine Learning using the pre-built solutions that can be deployed with just a few clicks. SageMaker JumpStart also supports one-click deployment and fine-tuning of more than 150 popular open-source models.

    • It is optimized for major frameworks

Amazon SageMaker is optimized for various popular deep learning frameworks such as TensorFlow, Apache MXNet, PyTorch, and more. Frameworks are always up-to-date with the latest version and are optimized for performance on AWS. Users don’t need to manually set up these frameworks and further can use them within the built-in containers.

    • It provides Local mode.

Amazon SageMaker enables users to test and prototype locally. The Apache MXNet and TensorFlow Docker containers used in the AWS SageMaker are available on GitHub. Users can download these containers and use the Python SDK to the test scripts before deploying to training or hosting.

What Users are saying..

profile image

Gautam Vermani

Data Consultant at Confidential
linkedin profile url

Having worked in the field of Data Science, I wanted to explore how I can implement projects in other domains, So I thought of connecting with ProjectPro. A project that helped me absorb this topic... Read More

Relevant Projects

SQL Project for Data Analysis using Oracle Database-Part 5
In this SQL Project for Data Analysis, you will learn to analyse data using various SQL functions like ROW_NUMBER, RANK, DENSE_RANK, SUBSTR, INSTR, COALESCE and NVL.

PySpark Project to Learn Advanced DataFrame Concepts
In this PySpark Big Data Project, you will gain hands-on experience working with advanced functionalities of PySpark Dataframes and Performance Optimization.

Build an Analytical Platform for eCommerce using AWS Services
In this AWS Big Data Project, you will use an eCommerce dataset to simulate the logs of user purchases, product views, cart history, and the user’s journey to build batch and real-time pipelines.

A Hands-On Approach to Learn Apache Spark using Scala
Get Started with Apache Spark using Scala for Big Data Analysis

Build Serverless Pipeline using AWS CDK and Lambda in Python
In this AWS Data Engineering Project, you will learn to build a serverless pipeline using AWS CDK and other AWS serverless technologies like AWS Lambda and Glue.

Build a Data Pipeline in AWS using NiFi, Spark, and ELK Stack
In this AWS Project, you will learn how to build a data pipeline Apache NiFi, Apache Spark, AWS S3, Amazon EMR cluster, Amazon OpenSearch, Logstash and Kibana.

Talend Real-Time Project for ETL Process Automation
In this Talend Project, you will learn how to build an ETL pipeline in Talend Open Studio to automate the process of File Loading and Processing.

Build Streaming Data Pipeline using Azure Stream Analytics
In this Azure Data Engineering Project, you will learn how to build a real-time streaming platform using Azure Stream Analytics, Azure Event Hub, and Azure SQL database.

Graph Database Modelling using AWS Neptune and Gremlin
In this data analytics project, you will use AWS Neptune graph database and Gremlin query language to analyse various performance metrics of flights.

How to deal with slowly changing dimensions using snowflake?
Implement Slowly Changing Dimensions using Snowflake Method - Build Type 1 and Type 2 SCD in Snowflake using the Stream and Task Functionalities