This three-course specialization moves beyond high-level theory to the gritty reality of managing modern AI stacks. It equips AI Product Managers, Technical Program Managers, Innovation Leads, and Governance Officers to handle the shift from deterministic software to probabilistic AI systems — navigating the trade-offs between model performance, inference costs, and safety to ensure AI initiatives survive the transition from Proof of Concept to production.

You will begin by architecting AI solutions using orchestration frameworks, vector databases, and RAG pipelines, building a functional chatbot MVP with LangChain, ChromaDB, and Streamlit. The second course shifts to production operations — mastering LLMOps workflows, prompt versioning, evaluation strategies including LLM-as-a-Judge metrics, and observability using tracing and drift monitoring tools. The final course prepares you to enforce safety and compliance through Red Teaming, guardrails implementation, explainability techniques, and regulatory navigation. By the end, you will be able to architect, operationalize, and govern AI systems with the rigor required for enterprise-scale deployment.

应用的学习项目

Throughout the specialization, learners complete applied projects that build on each other across all three courses. You will build a RAG-powered chatbot by ingesting documents with LangChain, storing embeddings in ChromaDB, and deploying through a Streamlit interface. You will then instrument that chatbot with tracing and evaluation tools, running test batteries to identify latency and cost bottlenecks using Weights & Biases and Arize Phoenix dashboards.

In the final project, learners Red Team their own chatbot using Giskard to generate vulnerability reports, then implement Guardrails AI to block identified attacks — producing a before-and-after audit log demonstrating successful adversarial defense. Learners work with industry tools including LangChain, ChromaDB, Streamlit, W&B, Arize Phoenix, Giskard, Guardrails AI, and Gliner across realistic enterprise scenarios — ensuring skills are practical, transferable, and immediately applicable to production AI environments.