FSD-3 DevOps for AI Applications
FSD-3 DevOps for AI Applications
- Course description
- Course CodeFSD-3
- Level of Study5.2
- Program of StudyFull Stack Development with AI
- Credits10
- Study Plan CoordinatorMonde Sineke
This course develops students' competencies in deploying, operating and maintaining AI-enabled applications that integrate existing AI services. Students learn deployment and operational practices, including containerisation, CI/CD, infrastructure as code and observability. They deploy applications, implement automated deployment processes and manage controlled releases, including updates to AI service integrations and changes to externally provided models. Students also assess their deployments against security and reliability requirements.
Students monitor application performance, logs, metrics and AI service usage to identify and troubleshoot operational issues. They evaluate the impact of changes to external AI services and implement improvements to reliability and performance. The course also covers operational cost management, including monitoring resource usage and AI service costs. Throughout the course, students document and justify their deployment and operational decisions.
The candidate...
- has knowledge of processes and tools for deploying and running AI-enabled applications, including containers, continuous delivery, infrastructure as code and observability
- can assess the security and reliability of their own deployments in relation to requirements for production systems
- has knowledge of how development and operations work together in the industry to keep AI enabled applications running
The candidate...
- can explain and document the deployment and operational choices for an AI-enabled application
- can assess an incident or performance problem in a deployed AI-enabled application, identify the cause and determine what measures need to be implemented
The candidate...
- can plan and carry out controlled releases of AI-enabled applications
- can contribute to organisational development by monitoring and reducing the running costs and resource use of AI systems
Students use a combination of written content, videos and practical learning activities designed to help them achieve the course learning outcomes.
Industry Relevance
Course content, learning activities and tools reflect industry standards and are developed in collaboration with industry partners and experts from the field.
Practical Work
The course includes regular practical activities where students apply their knowledge and practise relevant skills.
Collaboration
Collaboration is facilitated through the LMS and Teams, and students are encouraged to work together for peer support and shared learning.
Guidance
Teachers are available for guidance at scheduled times. Students are encouraged to share work in progress and use feedback from teachers and fellow students to develop their work further.
| Form of assessment | Grading scale | Grouping | Duration of assessment |
|---|---|---|---|
Course Assignment | Pass / Fail | Individual | 1 Week(s) |