Machine Learning Service - Amazon SageMaker AI
Build, train, and deploy machine learning models for any use case with fully managed infrastructure, tools, and workflows
What is Amazon SageMaker AI?
✔ To make it easier to get started, Amazon SageMaker JumpStart provides a set of solutions for the most common use cases that can be deployed readily with just a few clicks.
✔ Prepare, build, train, and deploy high-quality machine learning models quickly by bringing together a broad set of capabilities purpose-built for machine learning.
✔ Amazon SageMaker AI is available for free, for 2 months, as part of the AWS Free Tier program. Users can get access to 250 hours per month of ml.t3.medium notebooks usage with the Free Tier.
Getting Started with Amazon SageMaker AI
Amazon SageMaker AI JumpStart helps you quickly and easily get started with machine learning. The solutions are fully customizable and supports one-click deployment and fine-tuning of more than 150 popular open source models such as natural language processing, object detection, and image classification models. Popular solutions include:
Extract & Analyze Data
Automatically extract, process, and analyze documents for more accurate investigation and faster decision making.
Fraud Detection
Automate detection of suspicious transactions faster and alert your customers to reduce potential financial loss.
Churn Prediction
Predict likelihood of customer churn and improve retention by honing in on likely abandoners and taking remedial actions such as promotional offers.
Personalized Recommendations
Deliver customized, unique experiences to customers to improve customer satisfaction and grow your business rapidly.
Amazon SageMaker AI on the Free Tier
As part of the AWS Free Tier, you can get started with Amazon SageMaker AI for free. Your two month free trial starts from the first month when you create your first SageMaker AI resource. The details of the free tier for Amazon SageMaker AI are in the table below:
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Amazon SageMaker AI capability
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Free Tier usage per month for the first 2 months
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Product Pricing
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Studio notebooks, and on-demand notebook instances
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250 hours of ml.t3.medium instance on Studio notebooks OR 250 hours of ml.t2 medium instance or ml.t3.medium instance on on-demand notebook instances |
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RStudio on SageMaker AI
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250 hours of ml.t3.medium instance on RSession app AND free ml.t3.medium instance for RStudioServerPro app |
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Data Wrangler
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25 hours of ml.m5.4xlarge instance |
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Feature Store
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10 million write units, 10 million read units, 25 GB storage |
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Training
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50 hours of m4.xlarge or m5.xlarge instances |
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Real-Time Inference
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125 hours of m4.xlarge or m5.xlarge instances |
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Serverless Inference
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150,000 seconds of inference duration |
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Canvas
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160 workspace instance hours/month, and up to 10 model creation requests/month, each with up to 1 million cells/model creation request |
Learn More About Amazon SageMaker AI
BMW Group
The BMW Group, known for best-in-class luxury vehicles, uses a broad relationship across Amazon and AWS services to enhance every aspect of car design and functionality. The BMW Group’s portfolio is underpinned by Amazon and AWS technology, which powers more than 1,000 microservices that process more than 12 billion requests per day—while achieving 99.95 percent reliability.
Read the Case Study
Intuit
Intuit, known for its financial management solutions geared towards over 100 millions consumer and small business customers, is using Amazon SageMaker AI and Amazon Bedrock to combine cutting-edge technology with human tax-and-bookkeeping experts and deliver highly personalized customer experiences.
Read the Case Study
Itaú Unibanco
Itaú Unibanco, the largest private-sector bank in Brazil, needed to improve the speed, flexibility, and scalability of its machine learning (ML) infrastructure for its more than 3,200 ML users. To speed up ML processes for data scientists, Itaú used Amazon SageMaker Studio, an integrated development environment that provides a single web-based visual interface to access purpose-built tools to perform all ML development steps.
Read the Case Study
Nearmap
With its aerial camera technology, Nearmap offers organizations a dynamic lens to track structural and environmental changes over time. Nearmap upgraded from on-premises hardware to robust and scalable solutions from Amazon Web Services (AWS). Large, custom deep learning models, and the current trend toward large vision models, require the ability to dynamically scale up training that uses multiple machines at once.
Read the Case Study
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