AI-Assisted Development
Advanced AI Coding Agent Techniques
Advanced workflows with AI coding agents: subagents, custom commands, larger refactors, and agent-driven development.
73 instructor-led courses across applied AI, cloud and DevOps, data engineering, architecture, and modern software development. Every course is delivered live, in person or virtually, and can be customized to your team.
How to read levels Foundation: no prior experience needed · Practitioner: some working experience assumed · Advanced: for experienced practitioners
Hands-on generative AI for technical audiences, from prompting through building, agentic systems, AI-assisted development, and productionizing, plus an AI automation and no-code bridge.
Advanced workflows with AI coding agents: subagents, custom commands, larger refactors, and agent-driven development.
Use AI tools to refactor safely and raise code quality, with techniques for legacy code, tests, and review.
Build agents that connect to tools and data through the Model Context Protocol, with hands-on MCP server and client work.
Build AI-enabled apps and flows on Microsoft Power Platform using Power Apps, Power Automate, and Copilot Studio.
Hands-on development of LLM-powered applications using the OpenAI and Anthropic APIs, from first call to a working feature.
Design and orchestrate multiple cooperating agents, covering roles, communication, and coordination patterns.
Enable non-developers to build useful tools with AI coding agents, safely and within guardrails.
Deploy LLM applications to production and scale them, covering hosting options, cost, latency, and reliability.
Advanced patterns for production LLM applications: structured output, tool use, streaming, and robust error handling.
How to measure and monitor LLM application quality: evaluation methods, test sets, and production observability.
Get productive with AI coding assistants: effective use of GitHub Copilot and Claude Code in everyday development.
The core techniques of writing effective prompts, with patterns and hands-on practice for getting reliable results from LLMs.
What agentic AI is, how agents plan and use tools, and where autonomous systems help or hurt.
A plain-language introduction to how generative AI and large language models work, what they can and cannot do, and where they fit in real work.
Automate real work with AI using no-code tools, building practical workflows in Zapier, Make, and n8n.
Prompting techniques aimed at developers: structured prompts, tool and function calling, and integrating prompts into applications.
Build production-grade RAG systems that ground large language models in your own data. Hands-on labs cover embeddings, chunking, vector databases, retrieval pipelines, evaluation, and the security and cost concerns of running RAG in production.
AI literacy, strategy, practical productivity, and responsible AI and governance for leaders and knowledge workers.
A non-technical grounding in AI for leaders: what it is, what it changes, and how to think about applying it.
Establish and lead AI governance in your organization: policies, risk, roles, controls, and accountability, drawing on the major governance frameworks rather than any single standard.
Building and leading an AI strategy: opportunities, risks, roadmap, and organizational readiness.
Hands-on productivity with ChatGPT and Claude: practical use cases for writing, analysis, and daily work.
How generative AI creates business value, with the strategic and operational questions leaders need to answer.
Practical AI literacy for the whole workforce: everyday uses, good habits, and where to be careful.
Get real value from M365 Copilot across Word, Excel, Outlook, and Teams, with role-based use cases.
Lead teams through AI adoption: change management, new workflows, and building an AI-capable organization.
A non-technical guide to writing prompts that get useful, reliable results for business tasks.
The principles and practices of responsible AI: fairness, transparency, risk, and ethical use.
The transition from developer to architect: architecture fundamentals, design and modeling, enterprise architecture, and the durable thinking skills that keep technologists valuable through change.
The classic design patterns applied to real code, with guidance on when each helps and when it hurts.
A human-centered, iterative approach to solving the right problem, adapted for engineers.
Design microservices well: service boundaries, communication, data, and the tradeoffs versus a monolith.
Model complex domains with DDD: entities, aggregates, bounded contexts, and strategic design.
Enterprise architecture using the TOGAF standard: the ADM, artifacts, and how EA delivers value. Can align to certification.
The transition every senior developer eventually faces. Learn what software architects actually do, how to think in tradeoffs and architecture characteristics, how to make and defend decisions, and how to lead and communicate as an architect, with a personal roadmap to get there.
The core of modern software architecture: characteristics, styles, components, and making sound structural decisions.
A tour of major architecture styles and patterns, with the tradeoffs that guide when to use each.
The communication and influence skills architects live or die by: presenting, persuading, and leading without authority.
The durable thinking skills that keep technologists valuable through every technology shift. Doubles as a keynote.
The mental models behind good architecture: framing problems, systems thinking, and reasoning in tradeoffs.
Azure-led cloud foundations, the Azure platform, containers and Kubernetes, and DevOps and CI/CD.
Foundational AWS concepts aligned to the Cloud Practitioner certification: core services, security, and pricing.
Build and deploy applications on Azure using App Service and Functions, aligned to AZ-204 topics.
Build continuous integration and delivery pipelines with GitHub Actions, from first workflow to deployment.
A vendor-neutral grounding in cloud computing: service models, deployment models, and cloud-native basics.
Design and build cloud-native applications on Azure: containers, managed services, and scalable patterns.
Run Kubernetes on Azure with AKS: cluster setup, deployments, scaling, and operations.
The principles and practices of DevOps: culture, flow, feedback, and the delivery pipeline.
Implement end-to-end DevOps with Azure DevOps, aligned to the AZ-400 certification.
Provision and manage infrastructure as code with Terraform: configuration, state, modules, and workflows.
Container fundamentals with Docker: images, containers, registries, and building and running your first services.
Core Kubernetes concepts and hands-on work: pods, deployments, services, networking, and storage.
Cloud and Azure fundamentals aligned to the AZ-900 certification: core services, pricing, and governance.
Data foundations, data engineering on Azure, data warehousing and analytics, and NoSQL and non-relational data.
Build and orchestrate ETL and ELT pipelines with Azure Data Factory, from ingestion to transformation.
End-to-end data engineering on Azure aligned to DP-203: storage, pipelines, and processing.
Build data engineering pipelines on Databricks: Spark, Delta Lake, and production-ready workflows.
Design data warehouses and dimensional models: star schemas, facts, dimensions, and ETL loading.
How vector databases store and search embeddings to power semantic search and RAG.
Core data concepts on Azure aligned to DP-900: relational, non-relational, and analytics workloads.
Deliver analytics on Azure Databricks: notebooks, SQL analytics, and turning data into insight.
Design sound relational databases: normalization, keys, relationships, and practical schema modeling.
Write effective SQL: querying, joins, aggregation, and the T-SQL essentials for working with data.
Work with document and non-relational data using MongoDB and Azure Cosmos DB, and know when to choose NoSQL.
Programming foundations, the .NET and C# stack, modern web and JavaScript, and engineering practices like testing and clean code.
Deliver software the agile way: Scrum roles and events, backlogs, sprints, and Kanban flow.
Design and build RESTful APIs with ASP.NET Core Web API, including routing, models, and documentation.
Build server-rendered web applications with ASP.NET Core MVC: controllers, views, models, and routing.
Write cleaner, more maintainable code and run effective code reviews that raise team quality.
Access and manage data with Entity Framework Core: modeling, querying, migrations, and performance.
Build a full-stack application with a .NET back end and a React front end, wired together end to end.
A first course in programming: core concepts, logic, and writing your first working code.
Modern JavaScript from ES6 onward: let and const, arrow functions, modules, promises, and async/await.
Object-oriented programming with C#: classes, inheritance, interfaces, and solid OO design.
Practical Python programming: syntax, data structures, functions, and building real scripts and programs.
Build server-side applications and APIs with Node.js and Express: routing, middleware, and data.
Practice test-driven development and automated testing across unit, integration, and UI levels.
Add type safety to JavaScript with TypeScript: types, interfaces, generics, and practical adoption.