Tasks to Achieve Automatic Platform Scaling: Design of a pipeline to deploy the application, Design and deployment of the auto-scaling platform, Integration with external data platforms, Development of a machine learning environment, Migration of databases to the cloud and Networking platform.

Challenge: Unión Alcoyana aimed to ensure the availability of its services to customers, employees, and mediators through the deployment of an auto-scalable platform that adapts to traffic peaks, ensuring high availability, security, and regulatory compliance.

Principal Solutions 

We deployed an auto-scalable platform in the AWS cloud, incorporating process automation.

This enabled Unión Alcoyana to enhance organizational efficiency through better time management and cost reduction. The project utilized cloud solutions and technologies characterized by their ability to process vast amounts of information, management control tools, e-business integration, and rapid adaptability to change. 

• Deployment of an auto-scaling platform on AWS, integrated with external data platforms and machine learning.

• A highly available and scalable system, reducing downtime and deployment costs.

• Integration with other machine learning and data mining services, now utilized to build a data lake.

Machine learning technology is applied to implement two key solutions: business process automation and predictive analytics. 

In the context of business process automation, machine learning streamlines systems, reduces errors, accelerates delivery times, enhances quality, lowers costs, and simplifies business operations. This is achieved by integrating software tools, human expertise, and optimized processes into automated workflows. 

• Extending descriptive and diagnostic analytics to predictive analytics by adding statistical models. 

• Adding more data sources and facilitating their access. 

• Automating the exploitation of data sources to execute statistical models.