AI Solution Development

What You Will Learn

• 1 Learn the foundations of AI solution development, including core AI concepts, terminology, and the solution development lifecycle.
• 2 Understand how to define AI problems correctly by framing business needs, selecting appropriate problem types, and setting measurable success criteria.
• 3 Explore data strategy for AI projects, including data collection, preparation, quality assessment, and pipeline design.
• 4 Apply machine learning fundamentals to select, train, and evaluate models appropriate to different problem types.
• 5 Develop deep learning solutions using neural network architectures suited to image, text, and structured data tasks.
• 6 Build natural language processing solutions that analyze, classify, and generate human language for practical applications.
• 7 Implement AI solution architecture patterns, including microservices, APIs, and scalable infrastructure design.
• 8 Analyze model evaluation and responsible AI practices, including fairness, bias detection, explainability, and compliance.
• 9 Create end-to-end MLOps pipelines to automate model training, testing, versioning, and deployment workflows.
• 10 Understand how to integrate AI solutions into existing products and systems using APIs, SDKs, and event-driven patterns.
• 11 Apply AI solution monitoring and maintenance practices to track model performance, detect drift, and manage retraining cycles.
• 12 Develop a capstone AI solution by synthesizing all course concepts into a complete problem definition, architecture, and deployment plan.