Amazon Web Services Inc. said today it’s making its popular Amazon SageMaker artificial intelligence service cheaper to use.

Amazon SageMaker was launched in 2017 and makes it possible for developers to build and train machine learning models for analytical and predictive applications that run in the AWS cloud. The service eliminates much of the grunt work involved in building machine learning models by providing developers with access to numerous common algorithms and other tools that can accelerate the process.

The service supports Jupyter notebooks, which are open-source web applications used to share live code, including drivers, packages and libraries for common deep learning frameworks. With AWS SageMaker, it’s possible to launch a pre-built notebook for a specific application or use case, and then customize it according to the specific data set and schemas the developer wants to train their model on. The service can pull data from the Amazon Simple Storage Service or other sources.

In a blog post today, AWS Technical Evangelist Julien Simon said the company’s studies have shown that Amazon SageMaker already reduces the cost of building machine learning models.

“In fact, we found out that the total cost of ownership (TCO) of Amazon SageMaker over a three-year horizon is over 54% lower compared to other options, and developers can be up to 10 times more productive,” Simon said. “This comes from the fact that Amazon SageMaker manages all the training and prediction infrastructure that ML typically requires, allowing teams to focus exclusively on studying and solving the ML problem at hand.”

Amazon’s infrastructure helps to reduce costs even further, Simon said. That’s because SageMaker provides access to highly optimized versions of the most popular machine learning libraries, and a choice of central processing units and graphics processing units so customers can choose the most efficient option for their specific projects.

Now, customers are getting another boost. Amazon said it’s announcing a significant price reduction of up to 18% on all ml.p2 and ml.p3 GPU instances for the AWS SageMaker service. The cost reductions will be dated back to Oct. 1 and will apply to its US East (N. Virginia), US East (Ohio), US West (Oregon), EU (Ireland), EU (Frankfurt), EU (London), Canada (Central), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Seoul), Asia Pacific (Tokyo), Asia Pacific (Mumbai) and AWS GovCloud (US-Gov-West) regions.

Since its launch in 2017, Amazon SageMaker has been adopted by numerous big companies to assist with their machine learning projects. For example, Lyft Inc., the ride-hailing platform that challenges Uber Technologies Inc., uses SageMaker to reduce the training period of its machine learning models from a couple of months to just days, helping to streamline its development processes.

Lyft’s main interest in machine learning stems from its desire to develop autonomous vehicles and reduce its reliance on human drivers. The company said it aggregates over 10 terabytes of data each day to train its machine learning models, and that SageMaker saves it a significant amount of time and money.

“Using Amazon SageMaker distributed training, we reduced our model training time from days to couple of hours,” said Lyft ML Systems Engineer Alex Bain. “By running our ML workloads on AWS, we streamlined our development cycles and reduced costs, ultimately accelerating our mission to deliver self-driving capabilities to our customers.”

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