Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
8 Hour(s) 11 Minute(s)
Language
English
Taught by
Prof. Ryan Ahmed, Ph.D., MBA | 500,000+ Students | Best-Selling Instructor, Stemplicity Inc.
Rating
Course Overview
Avoid the 85% failure rate. Learn to plan, manage, build, & deploy AI projects that succeed in the real world.
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 :)
Course Content
- 6 section(s)
- 86 lecture(s)
- Section 1 Welcome Message, Instructor Introduction, & Course Outline
- Section 2 AI Fundamentals and AI Project Lifecycle Overview
- Section 3 AI Project Management: Stakeholders, Teams, and Strategic Planning
- Section 4 Data Strategy and Preparation in AI Projects Lifecycle
- Section 5 AI Models Development, Evaluation, Version Control, & Infrastructure
- Section 6 Congratulations and Thank You!
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.
Skills covered in this course
Reviews
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MMayuresh Bhanushali
good
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SSanjay Kumar Sinha
While I am just starting,the first impression I got is - The course is well designed , Optimized and provide a holistic view in this topic.
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KKatarzyna Pilarz
Very informative course, I recommend!
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RRajendra Nurukurthi
Superb learning Experience
