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Amazon Makes Machine Learning More Accessible To Developers

Nastel Technologies®
December 30, 2019

Amazon has recently announced that they have reached a new milestone in machine learning improvement. The company has proudly announced a brand new approach that will bring machine learning technology closer to developers across the globe.

Besides their already impressive collection of tools for the development of machine learning models, Amazon has now placed a new capability in the hands of thousands of developers.

“This announcement is ball 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,” says VP of artificial intelligence at AWS, Matt Wood.

Amazon Takes Machine Learning Further

Namely, Amazon now allows developers to combine tools such as Amazon QuickSight, Aurora, and Athena with SQL queries and thus access machine learning models more easily. In other words, developers can now access a wider variety of underlying data without any additional coding, which makes the development process faster and easier.

Amazon’s Aurora is a MySQL-compatible database that automatically pulls the data into the application to run any machine learning model the developer assigns it. Then, developers can use the company’s serverless system known as Athena to obtain additional sets of data more easily.

Finally, the last piece of the puzzle is QuickSight, Amazon’s tool used for creating visualizations based on available data. The combination of these three tools will provide a far more efficient approach to the development of machine learning models.

During the announcement, Wood also mentioned a lead-scoring model that developers can 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.

“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.

The Advantages of the New Approach

The new approach to using machine learning models has simplified the way developers use this technology. The wider access to data makes the entire development process faster, as there is no need for additional code sequences.

“By making sophisticated ML predictions more easily available through SQL queries and dashboards, the changes we are 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.

This article originally appeared on To read the full article and see the images, click here.

Nastel Technologies uses machine learning to detect anomalies, behavior and sentiment, accelerate decisions, satisfy customers, innovate continuously.  To answer business-centric questions and provide actionable guidance for decision-makers, Nastel’s AutoPilot® for Analytics fuses:

  • Advanced predictive anomaly detection, Bayesian Classification and other machine learning algorithms
  • Raw information handling and analytics speed
  • End-to-end business transaction tracking that spans technologies, tiers, and organizations
  • Intuitive, easy-to-use data visualizations and dashboards

Nastel Technologies is the global leader in Integration Infrastructure Management (i2M). It helps companies achieve flawless delivery of digital services powered by integration infrastructure by delivering tools for Middleware Management, Monitoring, Tracking, and Analytics to detect anomalies, accelerate decisions, and enable customers to constantly innovate, to answer business-centric questions, and provide actionable guidance for decision-makers. It is particularly focused on IBM MQ, Apache Kafka, Solace, TIBCO EMS, ACE/IIB and also supports RabbitMQ, ActiveMQ, Blockchain, IOT, DataPower, MFT, IBM Cloud Pak for Integration and many more.


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