AI Project Lifecycle Mastery: Strategy to Deployment - 2025

Avoid the 85% failure rate. Learn to plan, manage, build, & deploy AI projects that succeed in the real world.

What you'll learn
- Understand the full lifecycle of AI projects from initial concept and problem definition to model deployment and monitoring.
- Build a strong foundation in AI fundamentals, including traditional AI, generative AI, and autonomous AI agents.
- Learn the unique challenges and requirements of AI projects compared to conventional software development.
- Translate business problems into AI use cases by identifying high-value applications and clearly defining success metrics (KPIs).
- Master the art of building effective AI teams, including data scientists, ML engineers, domain experts, and project managers.
- Understand differences between structured vs. unstructured, labeled vs. unlabeled and choosing appropriate internal and external data sources.
- Design a comprehensive data strategy that includes data collection, governance, access control, and lifecycle management.
- Master data cleaning techniques, feature engineering, & dataset versioning. Understand the importance of data quality & labeling accuracy for model performance.
- Select suitable AI/ML models based on the problem type and data availability and learn the trade-offs of different architectures.
- Apply appropriate metrics (accuracy, F1 score, ROC AUC, etc.) to evaluate models. Use testing strategies and open-source leaderboards to benchmark performance.
- Understand MLOps practices such as CI/CD, model serving, monitoring, and automated retraining.
- Learn how to set up performance monitoring pipelines to track AI Models drift, errors, and model decay.
- Understand the ethical implications of AI. Learn to navigate legal frameworks, ensure fairness and transparency, and prevent bias.
- Use tools and Platforms like Pandas, Hugging Face, Kaggle, & Google Teachable Machines.
- Understand the differences between Databases, Data Lakes, and Data Warehouses for AI data storage.

Requirements
- A Laptop and Internet Connection is all you need. No programming experience is required!

Description
Welcome to the AI Project Lifecyle Course!

Research indicates that over 85% of AI projects fail to deliver on their promise.

This is because teams jump straight to building models without a clear strategy, plan, or understanding of the whole picture and the entire AI lifecycle end-to-end.

That’s where this course comes in.

The"AI Project Lifecycle: From Concept to Deployment"course is designed to help you bridge the gap between AI theory and real-world execution.

The course is designed for product managers, engineers, business leaders, or anyone curious about AI. It will give you a practical, step-by-step roadmap to manage AI projects from start to finish.

We’ll start with the fundamentals, like what AI, generative AI, and AI agents are, and walk through each phase of the lifecycle: defining business goals, building a strong data strategy, selecting and validating the right models, and deploying solutions that work in the real world.

We will then learn how to ensure ethical AI use, navigate governance and compliance, and avoid the common pitfalls that derail so many AI projects.

No prior coding or AI experience is needed. You'll gain hands-on exposure to tools like Pandas, SageMaker, Hugging Face, and Teachable Machine, and apply your learning through real-world case studies and practice challenges.

By the end of the course, you won’t just understand AI; you’ll know how to lead it!

Enroll today, and I look forward to seeing you on the other side!

Happy Learning :)

Who this course is for:
- Product Managers wanting to define AI product vision, set KPIs, and lead cross-functional teams through the AI lifecycle.
- Engineers & Data Scientists who want to master the full AI project lifecycle from data strategy to deployment, beyond just model building.
- Business Leaders & Analysts wanting to apply AI to improve decision-making, optimize operations, and gain competitive advantage.
- Project Managers who want to manage AI project timelines, risks, and resources. No technical background required.
- Executives & Innovation Leads who evaluate AI investments, align them with strategy, and drive successful adoption across the business.
- Students & Career Changers wanting to build practical, AI project management skills with no prior experience required.
More Info