AWS AutoML Solutions
AutoML automates each step of the ML workflow so that it’s easier to use machine learning. AWS provides a range of AutoML solutions for all levels of expertise. For ML practitioners seeking an open source solution we offer AutoGluon and for data scientists who prefer a fully-managed service, we offer Amazon SageMaker which automatically creates models based on your unique use case. Developers or business users without ML experience can take advantage of ready-made solutions for use cases such as computer vision, demand forecasting, intelligent search, and industrial and healthcare verticals.
Benefits
Spend time on what matters
With AutoML embedded into services, you don’t need to worry about data preparation, feature engineering, algorithm selection, training and tuning, inference, and continuous model monitoring. Instead you can remain focused on the work that has higher impact on your business outcomes.
AutoML for all
Whether you’re a business user, data scientist, or developer, AWS has AutoML solutions for you. From open source projects like AutoGluon for automated deep learning to Amazon Lookout for Metrics which automatically prepares business metrics data and detects anomalies – you can get started with no ML expertise required.
Full transparency
AWS makes it easy to dive deep into the models generated by AutoML. For example, Amazon SageMaker Autopilot ranks automatically generated models based on performance and, with just a few clicks, you can see how the model was created and what’s in it. SageMaker also gives you access to one-click deployment and tuning of more than 150 open source models.
End-to-end solutions
You can apply ML at scale using the 70+ end-to-end solutions. Solutions solve horizontal use cases including intelligent document processing and computer vision as well as vertical use cases for healthcare and industrial. All solutions can be started with just a few clicks.
What is AutoML?
Automatic machine learning, known as AutoML, removes the tedious, iterative, and time-consuming work across the machine learning workflow from data acquisition to model operationalization, so you can spend less time on low level details and more time on using ML to improve business outcomes. AutoML tools take care of sourcing and preparing data, engineering features, training and tuning models, deploying models, and ongoing model monitoring and updating.
AutoML for Open Source
AutoML for Amazon SageMaker
AI Services - Easily add intelligence to applications. No machine learning skills required
AutoML for Language
AutoML for Vision
AutoML for Business Metrics Analysis
AutoML for Code and DevOps
AutoML for Personalization
AutoML for Industrial
AutoML for Healthcare
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