An application architecture describes the patterns and techniques used to design and build an application. The architecture gives you a roadmap and best practices to follow when building an application so that you end up with a well-structured app. Application Architecture depicts different architecture aspects such as Functional Analysis, Implementation Architecture, Tools & Technology, Data, Non-Functional, Deployment Architecture, views of an Application. It enables you to envision the big picture and reduce cost by removing redundancies. Integrating components in the application and other systems are also clearly demarcated for everyone to visualize.

One of our customers is a German Automotive Company. We help the company steer towards a data-driven, AI-led company, by implementing and maintaining ML models on their platform. Their main objectives are data democratization and the culture of self service. We're talking about data from mileage, speed, consumption, RPM, engine faults, driver behavior, road driving patterns and car components that we need to process and carefully tailor for the machine learning models. To accomplish that, we're using S3, Glue, Lambda, Kinesis, Athena, Codepipeline, CLI, PySpark and Terraform together with AWS SageMaker for the machine learning solutions.

Machine Learning (ML) and Artificial Intelligence (AI) are buzzwords heard everywhere today, especially in the news about technology. It seems as everything today has some AI and ML properties. Marketers have even begun to use these terms to make their goods or services appear more attractive, making ML and AI such ubiquitous acronyms that it seems you can find them in the most unlikely of products.