Level: Beginner to Intermediate
Prerequisite: None
Leading and managing AI projects differs significantly from other technology endeavors. Successful execution of AI projects demands comprehensive team-based collaboration across the entire data science lifecycle, guided by robust processes with clearly defined roles and actionable tasks. The intricate nature of diverse techniques required to implement projects based on AI solution patterns, such as supervised machine learning versus unsupervised generative AI, further complicates AI project management.
This workshop offers templates and frameworks that facilitate specific actions that are aligned with AI solution patterns, enabling systematic identification, evaluation, and mitigation of risks in data science projects. The tools and techniques shared will provide guidelines for tasks to be performed by business analysts, project managers, and data scientists and ways to improve communication amongst them, resulting in enhanced project requirements and model reports—critical artifacts that deliver success in data science projects.
You Will Learn
- How to recognize the nuanced dynamics of data science project activities influenced by diverse solution types
- The roles and responsibilities for successful initiatives, and how to foster effective collaboration within cross-functional teams to deliver robust data science solutions
- A framework facilitating structured activities tailored to AI solution patterns
- Techniques for developing clear and comprehensive business requirements crucial for successful data science project implementation
- Strategies to mitigate business risks inherent in data science initiatives
- How to define AI-specific success criteria aligned with overarching business goals and project objectives
- Ways to assess model performance and deployment plan
Geared To
- Analytics leaders
- Business analysts
- Product managers
- Project managers
- Data science and analytics teams