At its AWS Summit which just concluded today, Andy Jassy, Senior VP at Amazon Web Services announced a new Machine Learning service today, aimed at putting Machine Learning within every developer’s reach.
The science of Machine Learning (often abbreviated as ML) provides the mathematical underpinnings needed to run the analysis and to make sense of the results. It can help you to turn all of that data into high-quality predictions by finding and codifying patterns and relationships within it. Properly used, Machine Learning can serve as the basis for systems that perform fraud detection (is this transaction legitimate or not?), demand forecasting, ad targeting and so forth.
AWS’s new service helps developers to use all of that data they’ve been collecting to improve the quality of 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.
Talking on the “Why” of introducing such a service Jassy emphasised that AWS wishes to help developers solve machine learning problems with expertise, without requiring any specific hard-core talent to do that.
So how do you go about using AWS Machine learning Service ? Well, to get a detailed know-how on AWS Machine learning Service, you view the relevant Amazon Web Services guide here.
AWS has also explained the entire MLS use case scenario with a fine example on its blog post for the same.
Amazon Machine Learning is available now and you can start using it today in the US East (Northern Virginia) region. Pricing, as usual, is on a pay-as-you-go basis:
- Data analysis, model training, and model evaluation will cost you $0.42 per compute hour.
- Batch predictions will cost $0.10 for every 1,000 predictions, rounded up to the next 1,000.
- Real time predictions cost $0.10 for every 1,000 predictions plus an hourly reserved capacity charge of $0.001 per hour for each 10 MB of memory provisioned for your model.
- During model creation, you specify the maximum memory size of each model to manage the cost and to control predictive performance.
- Charges for data stored in S3, Amazon RDS, and Amazon Redshift are billed separately.
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