Amazon Machine Learning vs Amazon SageMaker: What are the differences?
Amazon Machine Learning: Visualization tools and wizards that guide you through the process of creating ML models w/o having to learn complex ML algorithms & technology. This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure; Amazon SageMaker: Accelerated Machine Learning. A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.
Amazon Machine Learning and Amazon SageMaker belong to "Machine Learning as a Service" category of the tech stack.
Some of the features offered by Amazon Machine Learning are:
- Easily Create Machine Learning Models
- From Models to Predictions in Seconds
- Scalable, High Performance Prediction Generation Service
On the other hand, Amazon SageMaker provides the following key features:
- Build: managed notebooks for authoring models, built-in high-performance algorithms, broad framework support
- Train: one-click training, authentic model tuning
- Deploy: one-click deployment, automatic A/B testing, fully-managed hosting with auto-scaling
Apli, Cymatic Security, and FetchyFox are some of the popular companies that use Amazon Machine Learning, whereas Amazon SageMaker is used by Zola, SoFi, and Relay42. Amazon Machine Learning has a broader approval, being mentioned in 9 company stacks & 10 developers stacks; compared to Amazon SageMaker, which is listed in 12 company stacks and 6 developer stacks.