Advanced Data Processing: Batch, Real-Time, and Cloud Architectures for AI

As AI becomes more and more integrated into enterprise applications and workflows, architecting robust and scalable AI systems becomes even more important, particularly if you’re a data scientist or AI engineer. Beyond mastering machine learning techniques and technologies, an engineer working in AI needs to be able to leverage expertise in architecting AI and ML pipelines that achieve business outcomes at scale. In this course, instructor Kumaran Ponnambalam focuses on the big picture of bringing together models, data, applications, and infrastructure to create robust architectures. By the end of this course, you’ll be prepared to design, manage, and adhere to best practices for different architecture patterns—including batch, real-time, cloud, and hybrid.

Learning objectives
Understand the unique characteristics and design constraints for different types of AI architectures.
Define the key architecture elements of AI across batch, real-time, cloud, and hybrid.
Create an architecture for a given use case by analyzing requirements and choosing the right patterns and technologies.
Scale the architecture to increase concurrency, response times, and throughput.