Enabling Advanced AI and ML Solutions at New10 with AWS Sagemaker

The Challenge

With a goal to grow and improve its machine learning (ML) capabilities while keeping the highest security and reliability practices, New10 was lacking one unified standard for its entire ML process. It had been using Amazon Redshift to store its data and several other tools for processing and analysis, but Eraneos identified what the next steps should be: to help them in becoming more data-driven. We knew this could be done by further leveraging the cloud technologies of AWS, and more specifically, AWS Sagemaker.

The approach

The first port of call was a proof of concept that demonstrated the capabilities of AWS Sagemaker and what it could mean for New10. On the engineering side, we built the necessary infrastructure and security regulations in line with the company’s requirements. Sagemaker studio was set up on the data science side, which allowed us to build a process that would serve as a guidebook when training new machine learning models.

Through our company training, we also set up an AWS Sagemaker course for New10’s data team, which let them get acquainted with the environment while teaching them how to use the tool to its full potential.

The result

Using AWS Sagemaker alongside Eraneos’ support, New10 is now able to build and train Advanced AI and ML models in one central location. The end-to-end machine learning solution also means the company’s data team can store, process, and prepare data for analysis without needing programming skills. This allows it to quickly and efficiently test different business cases and put them into production. In terms of data science, the team can work faster and more efficiently on data transformations and visualizations, as well as spend less time on data quality and cleansing.