D791A_Integrating_AI_Modern_Software_Applications

Course Description and Competencies

What to Expect

In this course, you will work through four modules of material. Each module is broken down into smaller units. Each unit provides insight into key concepts in artificial intelligence integration. These include resource management, integration strategies, and performance metrics.

Throughout the course, you will find readings and other activities to strengthen your understanding of the content. These include videos, practice situations, and knowledge checks to help reinforce the knowledge you’ve gained. Engaging with these activities reinforces and expands new learning, complementing the knowledge you bring to this course.

The final assessment provides an opportunity to demonstrate your mastery of the competencies in this course. You may attempt the assessment two times before additional support is necessary. If you require further attempts, please contact your Student Experience Team. We're here to help!

Prerequisites:

To get the most out of this course, you should have a background in software development, including experience with coding and at least one major programming language. Familiarity with Python is also recommended, as it is widely used in AI tools and libraries.

Course Competencies

This course covers the following competencies:

Explains System Architecture Design: The learner explains the design of the system architecture to ensure AI systems can scale with increasing data and user demands.
Outlines Potential Risks and Mitigation Strategies: The learner outlines the potential risks and mitigation strategies for integrating AI into existing software applications.
Describes Data Used by AI System: The learner describes the data used by the AI system, its sources, and its characteristics.
Describes Deployment and Monitoring Methods: The learner describes methods for deployment and monitoring of AI systems.

Assessment

You must achieve a ranking of Competent on each assessment to pass the course. You may attempt the assessment two times before additional support is necessary. If you require further attempts, please contact your Student Experience Team.

1 Final Project

3 Competency Units

Course Outline

Module	Upon completion of this module, you will be able to:
AI System Architecture	Explain the impact of architecture patterns on the scalability of AI models. Explain the difference between vertical scaling and horizontal scaling in AI model development and discuss real-world challenges associated with scaling AI architecture. Evaluate resource management techniques in AI production environments and their impact on operational efficiency. Explain and select performance optimization approaches for AI models in production environments. Analyze AI-driven techniques for real-time resource management and the proactive monitoring of system performance bottlenecks. Compare and evaluate off-the-shelf and custom-built AI solutions in terms of ease of implementation and adaptability to specific business needs. Analyze how security considerations, integration costs, long-term maintenance, and technical debt influence the selection of off-the-shelf versus custom-built AI solutions.
Integration Strategies	Describe business disruptions related to AI integration, including security risks associated with integrating AI into business systems. Explain governance risks, including legal, ethical, and compliance, associated with AI integration. Examine technical risks associated with integrating AI into applications and adopt a risk assessment framework to categorize technical risks. Describe strategies that mitigate privacy concerns with AI systems. Examine strategies to ensure compliance with data protection regulations and safeguard user privacy in AI systems. Analyze internal and external threats to AI systems and implement appropriate security measures to mitigate risk.
Data Management and Processing	Explain the impact of incomplete data on AI model performance and describe strategies for managing incomplete data. Analyze and apply dataset validation principles to evaluate the accuracy, completeness, and distribution of data used in AI model training. Describe and evaluate data currency and data relevancy in AI models. Explain common sources for structured and unstructured data. Articulate the methods to ingest, transform, and process structured and unstructured data for AI systems.
System Deployment and Monitoring	Describe key components related to AI model deployment. Explain how to deploy an AI model in the cloud. Describe key performance metrics for AI model deployment. Describe methods for continuous monitoring of AI models after deployment. Describe rollback strategies that minimize downtime and preserve data integrity in the event of AI system failures during and after deployment. Explain resource optimization techniques to control AI deployment costs in the cloud.

Technology Requirements

We want to be sure you have the tools to succeed! Review the Computer System and Technology Requirements to learn about the technology you'll need. If you have questions about your setup, contact support@academy.wgu.edu.

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Key Contacts

Certificate Connect
Check out this online community to take advantage of course resources, including videos and tips from your educators. You can ask and answer questions, provide feedback on your progress, and interact with fellow students. You will find this platform in the Student Resources section of the course. Log on and do some exploring!

Technical Support
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Program Support
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Accommodations

WGU provides compliant and accessible learning experiences. If you require accommodation, please contact us at the start of the course. You can email disability@academy.wgu.edu.

We are committed to ensuring that all students with disabilities have equal access to WGU's services and materials. We strive to use best practices for accessibility. Our goal is to conform to existing U.S. laws, including the Americans with Disabilities Act and Section 504 and Section 508 of the Rehabilitation Act.

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