More news from the AWS Summit being held in San Francisco today as AWS announces a machine learning service for its customers. The need for this is obvious: it is becoming increasingly easy for organizations to collect vast amounts of data. In doing so they can create what is known as a data lake, a vast pool of undifferentiated data that can then be analyzed.
The issue is that the tools to actually analyze data are often expensive and complex - what organizations need is a simple set of tools to do this analytics and machine learning heavy lifting.
Enter AWS, who is following in the footsteps of its competitor,
As AWS pointed out in their blog post, the fact that it is increasingly easy to build a data lake is the good news,the bad news is that you need to find data scientists with relevant expertise in machine learning, hope that your infrastructure is able to support their chosen tool set, and hope (again) that the tool set is sufficiently reliable and scalable for production use.
Amazon's new service helps organizations use all of the data that they've been collecting. Users can build and tinker with their predictive models and then use the service to make predictions at scale.
Amazon Machine Learning is available now in the US East (Northern Virginia) region. Pricing, as is usual for AWS, is on a pay-as-you-go basis:
- Data analysis, model training, and model evaluation will cost $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 the model. During model creation, users 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.
Judging by the reaction of the audience at the summit, and on
