AI
Introduction to TERRAIN AI Framework
This short 4-hour course is an introduction to a larger 2-day training program, which equips you with the knowledge and tools to navigate the exciting and ever-evolving world of AI development. We’ll delve into the TERRAIN Framework – a comprehensive roadmap that empowers you to build impactful AI solutions within a flexible and iterative environment.
Duration
½ Day (4 hours)
Audience
This course is designed for all professionals, including:
● CIOs
● Program Managers
● Data Scientists
● Project Managers
● Scrum Masters
● Software Engineers
● Business Analysts
● Product Managers
● Product Owners
● Anyone interested in leveraging AI within a software development framework.
Prerequisites
Students should have a basic understanding of the software development life cycle and familiarity with methods and tools such as Agile, PMP, SDLC, Software Testing, business analysis, Scrum Master, product owner, etc.
This course will familiarize participants with:
- The unique challenges and considerations in managing AI
projects and introduce the typical stages of an AI project. - The seven crucial phases of the TERRAIN Framework and associated best practices.
- TERRAIN for actionable strategies for building high-performing Agile AI teams.
- Various techniques for ensuring data quality, ethical
considerations, and responsible AI development. - The importance of measuring success: Unique metrics for Agile AI projects.
About the Instructor
Andy Desai
Andy is an AI visionary with expertise in shaping and implementing business strategies to drive enterprise-level transformations. Andy has developed and deployed cutting-edge AI solutions to drive business growth, optimize operations, and enhance customer experiences. Andy is adept at building high-performing teams, fostering innovation, and leading cultural transformations. A published author, his TERRAIN AI framework is a collection of best practices and industry expertise that has the potential to reshape the way you do business!
Module 1: Introduction to AI Project Management
The AI Project Lifecycle: This section introduces the typical stages of an AI project, from
problem identification to model deployment and maintenance.
Integration and Deployment: Planning for smooth integration of the AI solution into existing
systems.
The TERRAIN Framework: short course
The TERRAIN Framework provides a structured yet adaptable approach to Agile AI
development. Each phase focuses on a critical aspect of the project lifecycle.
Module 2: T – Team Formation & Envisioning
Theme/Focus: Assemble a diverse and skilled team with the expertise needed for successful
AI development.
Key Takeaways: Aligned stakeholders, clear project vision, and a motivated team ready to
explore.
Module 3: E – Exploration & Data Acquisition
Theme/Focus: Identify and acquire high-quality data that fuels the AI model’s training and
evaluation.
Key Takeaways: A well-defined data strategy, clean and representative data for model training.
Module 4: R – Rapid Prototyping & Experimentation
Theme/Focus: Develop Minimum Viable Products (MVPs) to test core functionalities and
gather early user feedback.
Key Takeaways: Validated core concepts and early user insights for further development.
Module 5: R – Rigorous Model Training & Evaluation
Theme/Focus: Train and evaluate the AI model using robust techniques while ensuring
fairness and explainability.
Key Takeaways: A well-trained and evaluated AI model with fair and interpretable
decision-making processes.
Module 6: A – AI in the Loop & Feedback Integration
Theme/Focus: Integrate human expertise into the AI workflow and continuously incorporate
user feedback into the solution.
Key Takeaways: A human-centered AI solution that learns and improves based on user
interaction.
Module 7: I – Iteration, Refinement, & Deployment
Theme/Focus: Optimize the AI model for efficiency and scalability, prepare for production
deployment, and create user training materials.
Key Takeaways: A production-ready AI solution with robust deployment and user training
strategies.
Module 8: N – Navigate & Monitor
Theme/Focus: Continuously monitor the deployed AI model for performance drift and
potential biases. Adapt and refine the model based on ongoing data and user feedback.
Key Takeaways: A sustainable AI solution that adapts to changing environments and user
needs.
Module 9: Close-out
Aligning AI Projects with Business Goals: Ensuring projects address real problems and
support the organization’s objectives.
Is there a discount available for current students?
UMBC students and alumni, as well as students who have previously taken a public training course with UMBC Training Centers are eligible for a 10% discount, capped at $250. Please provide a copy of your UMBC student ID or an unofficial transcript or the name of the UMBC Training Centers course you have completed. Asynchronous courses are excluded from this offer.
What is the cancellation and refund policy?
Student will receive a refund of paid registration fees only if UMBC Training Centers receives a notice of cancellation at least 10 business days prior to the class start date for classes or the exam date for exams.