AI Architecture & Design Course

The AI Architecture course consists of five modules that cover fundamental and advanced AI systems and technology architecture topics, including design principles, distributed AI computing and scalability and reliability infrastructure, decision-making logic, performance optimization, security and enterprise architecture integration.

Each module has a set of lessons and is further supplemented with exercises and practice questions to help reinforce your understanding of key topics. Completing this course also prepares you for the official certification exam, as explained on the AI Architect Certification page.

Further resources are available to assist you with the completion of this course and preparation for the certification exam. These include downloadable digital course PDFs, printed course materials that can be shipped to your location, as well as coaching and instructor-led training services available by Arcitura and its training partner network. You can purchase coaching time on an hourly basis and instructor-led training workshops are available for individuals and groups.

This course is available via two separate eLearning platforms, each of which has different features and benefits. Upon clicking the Enroll button, you will be able to choose the eLearning option that works best for you.

Contact [email protected] with any questions.

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Workbook Lessons

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Video Lessons

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Reference Posters

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Interactive Exercises

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Graded Self-Test

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Completion Certificate

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Course Modules

MODULE 01 | Fundamental Predictive AI
This course module illustrates how predictive AI can be used and applied in a range of business applications, as well as essential coverage of predictive AI practices and systems. The module explores the most common learning approaches and functional areas that AI systems are used for. All of the content is authored in easy-to-understand, plain English.

MODULE 04 | Fundamental Generative AI
This course module explores the application of generative AI within a range of business scenarios, and provides fundamental coverage of generative AI concepts, models, best practices, and neural networks, including Generative Adversarial Networks (GANs), Variational Encoders (VAEs) and Transformer models. All of the content is authored in easy-to-understand, plain English.

MODULE 10 | Fundamental AI Architecture
Covers core frameworks and technology architecture and infrastructure of predictive and generative AI system implementations. Includes coverage of neural networks processing requirements and computational considerations pertaining to AI system model training and production processing, as well as AI system data flow and processing optimization and scalability.

MODULE 11 | Advanced AI Architecture
Provides an exploration of different AI system architecture designs and addresses complex topics such as hyperparameter tuning, advanced optimization strategies for large-scale neural networks. Also covers the intricacies of transfer learning and multi-modal AI systems, as well as distributed computing, explainability and adversarial robustness in AI models.

MODULE 12 | AI Architecture Lab
This course module provides a series of case-study driven, lab-style exercises and problems that are designed to test their ability to apply their knowledge of topics covered in previous modules. Completing this lab helps reinforce understanding of preceding topics and further demonstrates how different practices and technologies can be applied together as part of greater solutions.