AI Development Workflow with SPEC, TDD, CI

Are you looking to build reliable and maintainable AI applications but struggling with unstructured development processes? This course provides a clear, step-by-step guide to establishing a professional AI development workflow. By the end of this course, you will be able to design, develop, and test AI applications with confidence, ensuring they meet specifications, are robust against changes, and integrate seamlessly into deployment pipelines. You will gain a systematic approach to building AI solutions that reduces errors and improves overall quality. What you'll learn: * Understand foundational concepts of structured AI development workflows and best practices. * Apply Specification-driven Development (SPEC) to define clear requirements for AI systems. * Practice Test-Driven Development (TDD) principles for reliable AI code and prompt validation. * Implement Continuous Integration (CI) pipelines for automated testing and deployment of AI components. * Learn to effectively integrate and manage large language models (LLMs) like Claude within your workflow. * Explore modern development environment setup, including dependency management and version control for AI projects. * Develop basic MLOps patterns for monitoring and maintaining AI application quality and performance. This course begins by laying the groundwork with essential terminology and core principles, then progresses to practical application through a series of written explanations and code examples. You will read about how to apply SPEC, TDD, and CI methodologies to build a robust AI development 'harness'. This course is designed for beginners with no prior experience in structured AI development or MLOps. No specific prerequisites are required. Start your journey to building professional-grade AI applications today.