AI Project Development
Take your AI skills from theory to production with our comprehensive project development course. Learn how to design, build, and deploy complete AI solutions that solve real business problems and create value.
This capstone course brings together all aspects of AI development, from initial problem definition through model deployment and monitoring. Work on real-world projects and build a professional portfolio that showcases your abilities to potential employers.
What You'll Learn
- AI project planning and problem definition
- Data collection, cleaning, and preparation at scale
- Experiment tracking and model versioning
- Model selection and hyperparameter optimization
- Production deployment with Docker and Kubernetes
- API development with FastAPI and Flask
- MLOps practices and continuous integration
- Model monitoring and maintenance in production
- Cloud deployment on AWS, GCP, and Azure
- Building complete AI-powered applications
Course Overview
AI Project Development is the ultimate course for anyone serious about building production-ready AI systems. This course bridges the gap between academic knowledge and industry practice, teaching you the skills needed to develop and deploy AI solutions in real-world environments.
You'll work on multiple end-to-end projects spanning different industries and use cases. Each project takes you through the complete development lifecycle, from initial concept to deployed application. You'll learn industry best practices, common pitfalls to avoid, and how to make technical decisions that balance performance, cost, and maintainability.
Duration: 12 weeks (10-12 hours per week)
Level: Advanced
Prerequisites: Strong Python skills, machine learning knowledge, and experience with deep learning frameworks
Certificate: Professional certificate with portfolio upon completion
Course Curriculum
Weeks 1-3: Project Planning and Data Engineering
Defining AI problems, data strategy, building data pipelines, and establishing project infrastructure.
Weeks 4-7: Model Development and Optimization
Experiment tracking, model selection, hyperparameter tuning, and performance optimization for production environments.
Weeks 8-12: Deployment and MLOps
Containerization, API development, cloud deployment, monitoring, and maintaining AI systems in production. Complete capstone projects.
Your Instructor
Learn from AI engineers and architects who have built and deployed production AI systems at scale. Our instructors have experience across multiple industries and have led AI initiatives from conception to successful deployment.
Receive mentorship on your projects, code reviews, and architectural guidance. Join our alumni network of AI professionals working at leading technology companies worldwide.