AI Development Strategy offers a comprehensive, one-day exploration of the essential aspects of developing and deploying artificial intelligence systems in real-world scenarios. The course equips students with the knowledge and skills they need to navigate the complexities of AI development, from resource management to post-deployment monitoring. Participants learn to identify crucial resources for AI development and build strategies for working in resource-constrained environments.
The curriculum covers structured AI development processes, including the CRISP-DM methodology, and explores deployment patterns and stakeholder engagement techniques. Students also gain insight into common anti-patterns to avoid and best practices for monitoring AI models in production. The course addresses the entire lifecycle of AI development, from initial planning to post-deployment maintenance. This prepares professionals to lead and contribute to AI projects in diverse organizational settings.
Through case studies and interactive workshops, participants gain experience crafting AI development strategies tailored to their own business needs. This helps AI initiatives succeed and deliver measurable value. By the end of the course, participants can lead AI development efforts within their organizations. They also navigate the challenges of AI development and integration with confidence.
The Resource-Constrained module works through three real limitations in turn. Attendees practice working without elements of the development pipeline, working with limited compute, and working with limited data. This builds judgment for projects that never have ideal conditions. The next module turns to deployment, comparing AI deployment patterns, engaging with stakeholders, and naming common anti-patterns that derail otherwise sound projects. The closing module, AI After Deployment, covers monitoring systems and the metrics and thresholds that trigger alerts. It also covers handling data drift so models stay accurate once real traffic starts flowing through them.
Who Should Attend
Technology Management, Developers, Architects, Engineering Managers and Data Science personnel
What Attendees Will Learn
Upon completing AI Development Strategy, participants will be able to:
- Identify the key resources for AI development
- Learn strategies for how to approach AI development in the absence of key resources
- Understand how to formulate a structured AI development process
- Explore methods for deploying AI models to production
- Develop strategies for monitoring AI models in production
- Foster collaboration between AI developers and stakeholders
Prerequisites
There are no formal prerequisites for this course; a general background in AI/ML development or technology management is helpful.