Agentic AI Development & Security

Architect Reusable AI Agent Systems is an intermediate course for AI engineers and developers who want to move beyond building single-purpose agents and learn to design scalable, modular agent architectures that drive business value. This course teaches advanced system design principles, equipping you to build maintainable AI systems that can evolve with business needs. You will learn to evaluate competing reasoning-loop architectures like ReAct and Reflexion by running data-driven A/B tests to select the optimal design for a specific use-case. Through hands-on labs, you will master the software engineering best practices for creating reusable agent components—the Planner, Memory, and Executor—by defining clear API contracts with typed interfaces. You will leave this course with the ability to design, document, and implement a complete Python package of agent components, ready for integration into a production environment.

"Design Ethical AI Rewards and Policies" is an engaging course for professionals, data scientists, AI practitioners, and decision-makers seeking to implement responsible AI practices in their organizations. In an era where AI systems significantly impact business operations, understanding how to balance performance metrics with ethical considerations is crucial. This course provides a comprehensive foundation in the principles of reinforcement learning, focusing on creating effective reward functions that align with business goals while ensuring compliance with ethical standards. Through hands-on labs, interactive dialogues, and real-world case studies, you will learn to identify and mitigate biases in AI policies, including adherence to global regulatory frameworks like GDPR. By integrating theory with practical application, this program equips you with the skills to lead initiatives that prioritize fairness and accountability in AI development. Whether your goal is to enhance customer interactions or ensure ethical governance, this course lays the groundwork for building trustworthy AI systems that deliver value without compromising integrity.

Engineer & Explain AI Model Decisions is an Intermediate-level course designed for Machine Learning and AI professionals who need to build trustworthy and justifiable AI systems. In today's complex data environments, high accuracy is not enough; you must be able to prove why a model made its decision and remediate biases that cause real-world harm. This course empowers you to combine advanced feature engineering and model interpretability practices to ensure ethical, reliable deployment. You will begin by mastering data transformation, learning to clean chaotic, conversational logs (like agent chat history) and converting them into structured, model-ready tensors using Python, scikit-learn, TF-IDF, and embedding aggregation. Further, you will dive into the "black box" using powerful explainability techniques like SHAP to analyze model reasoning. You will run diagnostics on misclassified examples, flag spurious correlations (such as time-of-day dependencies), and develop strategies for bias remediation. The final deliverable is an AI Model Decision Toolkit, culminating in a stakeholder-ready interpretability report that translates technical findings into actionable, business insights. This course is essential for anyone responsible for the transparent, reliable, and bias-aware deployment of AI in production.

Optimize AI: Build Reusable Model Pipelines is an intermediate course for machine learning engineers and data scientists aiming to create efficient, scalable, and maintainable AI workflows. In a world of rapidly evolving models, choosing the right one is only the beginning. This course moves beyond model selection to focus on the critical next step: building standardized, reusable pipelines that ensure consistency and accelerate development. You will learn to strategically evaluate the trade-offs between large, pre-trained models and smaller, custom-built alternatives, balancing performance with real-world constraints like inference speed and cost. Through hands-on labs, you will master the art of constructing modular and reproducible ML pipelines using Scikit-learn. The curriculum emphasizes best practices for model management and versioning, empowering you to design robust systems that are easy to update, debug, and deploy. By the end of this course, you will be equipped to move from ad-hoc model development to a systematic, pipeline-driven approach that is essential for building professional, production-ready AI solutions.

Secure AI: API and Dependency Risks is an intermediate course that moves you from developer to defender, teaching you the essential practices for building production-grade, secure AI. You will learn to implement OWASP ASVS guidelines to harden API endpoints with critical controls like JWT authentication, input validation to prevent injection attacks, and rate limiting. Then, you will adopt an attacker’s mindset, using DAST tools like OWASP ZAP to verify your defenses are effective. Next, you will master the art of managing your software supply chain by analyzing dependency vulnerability reports, using the CVSS framework to prioritize real threats over noise, and formulating verified hotfix and rollback plans. Through hands-on labs simulating real-world security incidents, you will leave this course ready to build, deploy, and maintain resilient AI services that can withstand modern threats.

Learners will demonstrate mastery by completing a Secure AI Testing Toolkit, where they will evaluate a dependency update, run integration tests, and document their findings, while developing a comprehensive testing suite with pytest that achieves at least 88% coverage. As part of this process, learners will evaluate a sample PR upgrading LangChain from version 0.1.5 to 0.1.8. Working in an off-platform Python environment, they will review changelogs for deprecated features, run security scans to identify vulnerabilities, and perform integration tests to validate compatibility. They will submit a structured report that includes an evaluation of a LangChain upgrade, a testing strategy documentation, and a reflection on the CI/CD pipeline improvements. Throughout the course, learners will engage in hands-on labs, guided coding exercises, in-video questions, interactive dialogues, and scenario-based video quizzes to apply their skills to real-world challenges. The final submission works as a personalized security and testing resource that enables learners to safeguard AI code, improve long-term reliability, and prove readiness to apply critical testing practices in professional AI development environments.

Hybrid AI Search Workflows is an intermediate course designed for AI engineers, developers, and data scientists who want to build search systems that are both intelligent and factually reliable. While generative AI offers powerful reasoning, it can be prone to hallucination; traditional search is accurate but lacks contextual understanding. This course teaches you how to architect and implement hybrid workflows that get the best of both worlds, grounding powerful LLMs with verifiable data from classic search techniques. You will move beyond basic prompting to design, evaluate, and optimize systems that balance performance and cost. Through hands-on labs, you will master techniques for tuning model parameters, designing cost-efficient hybrid architectures, and modularizing your code for scalable, production-ready CI/CD pipelines. By the end of this course, you will be equipped to build and deploy trustworthy AI search applications that deliver accurate, context-aware, and reliable answers.

Optimize Agentic AI: Algorithms for Peak Performance is an intermediate course for AI practitioners and engineers aiming to build fast, scalable, and responsive agentic systems. In the real world, an AI agent's intelligence is useless if it is too slow. This course equips learners with the tools to diagnose and solve these critical performance bottlenecks. You will learn to master essential algorithm optimization techniques, moving beyond slow, brute-force methods. Through hands-on labs, you will replace baseline planners with sophisticated informed search algorithms such as beam search and quantitatively measure the dramatic improvements in planning time. You will also learn to analyze the computational complexity of multi-tool reasoning pipelines using Big-O notation, using profilers to pinpoint the exact functions and data structures that create system slowdowns. By the end of the course, you will not only be able to implement critical optimizations—such as using an index to reduce complexity from O(n²) to O(log n)—but also to write a professional technical proposal to justify your engineering decisions.

"Optimize Python for Agentic AI" is an intermediate course for developers who want to elevate their Python code from functional to professional-grade. In the world of AI, inefficient or unreadable code can cripple an agent's performance and slow down team collaboration. This course equips you with the essential software engineering practices to write Python that is both highly efficient and exceptionally clear. You will learn to apply clean-code conventions, including PEP 8 standards, type hints, and descriptive docstrings, to produce readable and maintainable modules that your teammates can easily understand and build upon. Through hands-on labs, you will master the art of performance tuning by systematically using profiling tools like cProfile to analyze runtime behavior, pinpoint hidden bottlenecks, and refactor code for significant speed improvements. By the end of this course, you will be able to confidently balance readability with runtime efficiency, ensuring the AI systems you build are not only intelligent but also robust, scalable, and production-ready.