Managing AI Systems: Development, Deployment, and Governance

Board Infinity via Coursera Specialization

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Provider

Coursera Specialization
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

12 weeks, 5 hours/week
Level

Intermediate

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Overview

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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.

Syllabus

Course 1: AI Systems Design: RAG Pipelines and LLM Architecture
Course 2: MLOps and LLMOps: Deploying and Scaling AI in Production
Course 3: AI Risk and Compliance: Audit and Governance Foundations

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This advanced course provides a practical, end-to-end approach to governing, securing, and auditing AI systems in enterprise environments. Learners begin by examining adversarial threats to AI systems—including jailbreaks, prompt injection, data leakage, manipulation, and misinformation attacks—and practice structured red teaming using both manual and automated techniques. Participants learn how to analyze vulnerability severity and exploitability, prioritize remediation, and evaluate AI system readiness under adversarial conditions while communicating findings through clear, audit-ready documentation. The course then explores regulatory and governance frameworks, focusing on the EU AI Act and the NIST AI Risk Management Framework (Govern, Map, Measure, Manage). Learners analyze AI system classifications, risk tiers, and obligations, and apply NIST AI RMF principles across the AI lifecycle. The course also covers key legal and compliance risks, including copyright, licensing, and data usage concerns in training data and outputs, and guides learners in creating concise compliance documentation and policies aligned with EU AI Act and NIST AI RMF requirements. Learners dive into explainability for LLMs and other AI models, exploring challenges and techniques such as SHAP, LIME, and attention visualization. They apply these tools to generate human-readable explanations, and critically evaluate the faithfulness, reliability, and quality of these explanations for different stakeholders. Finally, the course turns to safety engineering and organizational governance, including implementing guardrails frameworks (e.g., Guardrails AI, NVIDIA NeMo) and using Presidio for PII detection, masking, and anonymization in AI and RAG pipelines. Learners assess Shadow AI risks and design governance strategies, monitoring, and control architectures that mitigate unsafe AI usage, document vulnerabilities, and support continuous regulatory compliance. Disclaimer: This is an independent educational resource created by Board Infinity for informational and educational purposes only. This course is not affiliated with, endorsed by, sponsored by, or officially associated with any company, organization, or certification body unless explicitly stated. The content provided is based on industry knowledge and best practices but does not constitute official training material for any specific employer or certification program. All company names, trademarks, service marks, and logos referenced are the property of their respective owners and are used solely for educational identification and comparison purposes.
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Design and prototype enterprise-grade AI solutions that create real business value. In this course, you’ll learn to distinguish when to use predictive ML versus generative AI, align AI initiatives with business outcomes, and define success criteria that balance accuracy, latency, safety, and cost. You’ll compare traditional deterministic software with probabilistic AI systems to understand where AI is appropriate—and where it isn’t. You’ll diagram modern AI system architectures and evaluate build-versus-buy decisions for key components such as models, vector databases, and orchestration layers. Through hands-on work, you’ll implement data ingestion pipelines, chunking and embedding strategies, and retrieval flows, and you’ll prepare messy, unstructured enterprise data for use in AI systems. You’ll analyze orchestration patterns including tools, chains, and agents and learn when to apply each. The course culminates in building an end-to-end retrieval-augmented generation (RAG) prototype with an interactive Streamlit UI. You’ll experiment with cost–quality trade-offs, compare RAG with fine-tuning for different use cases, and use logs to iteratively test and refine your MVP. By the end, you’ll be able to demonstrate both technical viability and business feasibility for AI solutions within an enterprise context. Disclaimer: This is an independent educational resource created by Board Infinity for informational and educational purposes only. This course is not affiliated with, endorsed by, sponsored by, or officially associated with any company, organization, or certification body unless explicitly stated. The content provided is based on industry knowledge and best practices but does not constitute official training material for any specific employer or certification program. All company names, trademarks, service marks, and logos referenced are the property of their respective owners and are used solely for educational identification and comparison purposes.
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This intermediate course equips ML engineers, data scientists, and software engineers with the practical skills needed to design, deploy, and scale production AI systems. You’ll learn how to architect reliable ML and LLM applications, including model serving patterns, feature stores, and retrieval-augmented generation (RAG) components. The course walks through reproducible training and experimentation pipelines with tools like MLflow and Weights & Biases, from experiment tracking and model registration to production deployment. You will configure CI/CD workflows tailored to ML and LLM systems, covering data, model, and prompt versioning, automated testing, and safe rollback strategies. The course emphasizes security, privacy, and compliance best practices, including access control, secrets management, and safe handling of user and training data. You’ll design scalable serving infrastructure using containers, Kubernetes, and autoscaling, and apply deployment patterns such as canary, blue-green, shadow, and A/B testing to introduce changes safely. Finally, you’ll build automated evaluation and observability for production AI. This includes automated evaluation pipelines (e.g., LLM-as-a-judge) wired into CI/CD gates, defining and tracking key quality and performance metrics like hallucination rate, latency, throughput, and cost per request, and implementing robust logging, metrics, distributed tracing, and telemetry. You will also detect and monitor data and model drift, bias, and degradation over time using tools such as Arize Phoenix, design alerting strategies, and collaborate with product and reliability teams to establish incident response, runbooks, and continuous improvement processes for AI systems at scale. Disclaimer: This is an independent educational resource created by Board Infinity for informational and educational purposes only. This course is not affiliated with, endorsed by, sponsored by, or officially associated with any company, organization, or certification body unless explicitly stated. The content provided is based on industry knowledge and best practices but does not constitute official training material for any specific employer or certification program. All company names, trademarks, service marks, and logos referenced are the property of their respective owners and are used solely for educational identification and comparison purposes.