Today, Amazon announced a new approach that it says will put machine learning technology in reach of more developers and line of business users. Amazon has been making a flurry of announcements ahead of its re:Invent customer conference next week in Las Vegas.
While the company offers plenty of tools for data scientists to build machine learning models and to process, store and visualize data, it wants to put that capability directly in the hands of developers with the help of the popular database query language, SQL.
By taking advantage of tools like Amazon QuickSight, Aurora and Athena in combination with SQL queries, developers can have much more direct access to machine learning models and underlying data without any additional coding, says VP of artificial intelligence at AWS, Matt Wood.
“This announcement is all about making it easier for developers to add machine learning predictions to their products and their processes by integrating those predictions directly with their databases,” Wood told TechCrunch.
For starters, Wood says developers can take advantage of Aurora, the company’s MySQL (and Postgres)-compatible database to build a simple SQL query into an application, which will automatically pull the data into the application and run whatever machine learning model the developer associates with it.
The second piece involves Athena, the company’s serverless query service. As with Aurora, developers can write a SQL query — in this case, against any data store — and based on a machine learning model they choose, return a set of data for use in an application.
The final piece is QuickSight, which is Amazon’s data visualization tool. Using one of the other tools to return some set of data, developers can use that data to create visualizations based on it inside whatever application they are creating.
“By making sophisticated ML predictions more easily available through SQL queries and dashboards, the changes we’re announcing today help to make ML more usable and accessible to database developers and business analysts. Now anyone who can write SQL can make — and importantly use — predictions in their applications without any custom code,” Amazon’s Matt Asay wrote in a blog post announcing these new capabilities.
Asay added that this approach is far easier than what developers had to do in the past to achieve this. “There is often a large amount of fiddly, manual work required to take these predictions and make them part of a broader application, process or analytics dashboard,” he wrote.
As an example, Wood offers a lead-scoring model you might use to pick the most likely sales targets to convert. “Today, in order to do lead scoring you have to go off and wire up all these pieces together in order to be able to get the predictions into the application,” he said. With this new capability, you can get there much faster.
“Now, as a developer I can just say that I have this lead scoring model which is deployed in SageMaker, and all I have to do is write literally one SQL statement that I do all day long into Aurora, and I can start getting back that lead scoring information. And then I just display it in my application and away I go,” Wood explained.
As for the machine learning models, these can come pre-built from Amazon, be developed by an in-house data science team or purchased in a machine learning model marketplace on Amazon, says Wood.
Today’s announcements from Amazon are designed to simplify machine learning and data access, and reduce the amount of coding to get from query to answer faster.
AWS releases SageMaker to make it easier to build and deploy machine learning models
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