Cloud AI Architecture Course

Cloud AI Architecture & Design Course (Pre-Order)

The Cloud AI Architecture course consists of three modules that cover the technology architecture of cloud-based AI systems, including cloud automation and infrastructure relevant to AI processing. The Cloud AI Fundamentals course is a prerequisite for this course.

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 Cloud 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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Inquire About Instructor-Led Training for Your Team

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 19 | Fundamental Cloud AI Technology & Automation
This course module focuses on cloud computing technology, infrastructure and practices specific to establishing and running cloud-based predictive AI and generative AI solutions. Topics include GPU and TPU for AI learning workloads, cloud-based AI services such as AWS SageMaker, Azure Machine Learning, and Google AI Platform for model development, training, and deployment, as well as mechanisms for scaling AI applications in the cloud, data storage, and pipeline options.

MODULE 20 | Advanced Cloud AI Technology & Automation
This course module covers a range of advanced topics, including cloud-based AI model management, AI pipeline orchestration, optimization techniques, scaling and failover, MLOps, as well as multi-cloud and hybrid AI strategies. Also covered are cloud-based data pipeline services, including AWS Glue, Azure Data Factory and Google Dataflow.

MODULE 21 | Fundamental Cloud Architecture & Design
This course module explores cloud-based architectural models and design patterns specific to predictive AI and generative AI applications, including the selection and configuration of specialized AI cloud infrastructure, AI-optimized compute instances and network topologies for data-intensive workloads, as well as strategies for integrating AI services within existing cloud environments. Topics also include the utilization of containerization and multi-clouds, as well as scalability, failover and security considerations.

MODULE 22 | Advanced Cloud AI Technology & Automation
This course module delves into complex architectural models and design patterns, as well as deployment strategies and optimization techniques for AI solutions. Topics covered include designing for high availability and fault tolerance in AI applications, implementing sophisticated data streaming architectures for real-time AI analytics, as well as exploring the utilization of containerization and microservices for AI systems.

MODULE 23 | Cloud AI Architecture Lab
This course module provides a series of case-study driven, lab-style exercises and problems that are designed to test your ability to apply your 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.