Product Development for AI & Data Products
- Fast-Track Learning — Master Key Concepts in a Half-Day Session
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- Practical Insights — Real World Examples
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Adapt agile practices for AI and data products: exploring AI vs. traditional development, implementing ethical AI strategies, prioritizing data-centric backlogs, and leading successful AI initiatives.
About Product Development for AI & Data Products
Elevate your product development expertise with our 1-day workshop designed to help you adapt agile practices for the unique demands of AI and data-driven projects. Dive into the critical distinctions between traditional software and AI development, learn strategies for ethical and responsible AI, and discover new ways to build and prioritize backlogs for data-centric products. Join us to gain the skills needed to lead successful AI initiatives in today’s fast-evolving digital landscape.
Product Development for AI & Data Products
Workshop Overview
Features
- One-day workshop
- Downloadable training materials
- Interactive sessions, examples and real-time instruction
What You Learn
- Understand AI and ML basics, and their differences from traditional software development
- Apply Agile methodologies to AI projects, addressing unique challenges
- Implement ethical AI practices, focusing on bias, transparency, and accountability
- Define roles in AI product development and collaborate with cross-functional teams
- Manage and prioritize AI product backlogs, focusing on model performance and continuous improvement
What You Earn
- Certificate of completion
- Expand career advancement opportunities in all industries
- Network of industry leaders and Agile professionals
- Access to free monthly events and resources
Who Should Attend?
- Executive and Strategic Leaders overseeing AI initiatives and seeking agile delivery approaches
- Product Managers and Product Owners responsible for guiding AI product vision, execution, and prioritization
- Scrum Masters and AI/Data Project Managers supporting agile processes in AI projects
- Data Scientists and Machine Learning Engineers developing AI models and optimizing workflows in an agile environment
- Software Engineers, UX Professionals, and Business Analysts working on AI-driven or data-intensive products
Detailed Agenda
Welcome & Course Overview
- Introductions and objectives
- Overview of AI product development: Current trends, challenges, and key distinctions from traditional software development
- Goals for the day
Section 1: Understanding AI, ML, and Data Product Development Basics
- Key concepts: AI, machine learning, deep learning, and data products
- Differences between AI, data-centric products, and traditional software
- Unique development lifecycle and stages of an AI product
Section 2: Adapting Agile Practices for AI Projects
- Scrum and Agile methodologies in AI: what works, what needs adaptation
- Addressing AI-specific challenges with agile practices: Data dependencies, model training, and iterative validation
- Creating agile workflows for data science teams and engineering teams
Section 3: Ethical and Responsible AI
- Importance of ethics in AI product development
- Addressing bias, transparency, and accountability
- Frameworks and best practices for responsible AI implementation
Section 4: Roles and Responsibilities in AI Product Development
- Product Owner’s role in AI projects vs. traditional software projects
- Stakeholder engagement: Understanding expectations and managing uncertainty
- Working with data scientists, ML engineers, and cross-functional teams
Section 5: Building and Prioritizing the AI Product Backlog
- Identifying key stories for AI features: Data acquisition, feature engineering, model training, and evaluation
- Prioritizing based on experiment outcomes and model accuracy needs
- Techniques for hypothesis-driven development and managing iteration cycles
Section 6: Metrics, Validation, and Continuous Improvement
- Defining success for AI projects: Accuracy, precision, recall, and other relevant metrics
- Managing continuous feedback loops and adapting based on model performance
- Continuous improvement in AI products: Updates, retraining, and refining
Wrap-Up & Q&A
- Summary of key takeaways
- Q&A and final discussion: Open floor for real-world applications and challenges
Why Choose Hyperdrive?
You’ll learn from the industry’s finest instructors and be able to immediately apply real-world scenarios to your work in the most interactive, thought-provoking, and exciting training on the market. Join 15,000+ satisfied students and enjoy exclusive benefits like:
Special discounts are available for alumni and military members — contact us for more details!
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We win “Best Silicon Valley Training Co.” year after year.
Enjoy FREE access to the largest and most vibrant training community in North America for top-tier continuing education and networking opportunities.
Featured Resources
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