Begin 2027 with
a new vision.

Learn the job AI companies are hiring for: work beside a customer, scope their problem and ship a working AI solution in their environment.

Win and lead customer AI projects: discovery, the business case, a first prototype with engineers, and adoption after go live.

Design agents that plan, use tools and work through tasks on their own, then keep them reliable, safe and affordable in production.

Use Claude Code, Anthropic's agentic coding tool, to plan, write, test and ship real projects from your terminal or editor.

Write prompts that get reliable results from any AI model, and turn your best ones into reusable templates for your team.

What AI, machine learning and large language models actually do, where they fail, and how to use them safely at work.

Hands on use of AI assistants for reports, analysis, email and research, using the tools your company already allows.

Instructor led prep across predictive, agile and hybrid delivery, with the 35 contact hours PMI requires.

Requirements, process mapping, user stories and stakeholder work, taught the way BA roles run day to day.

How models move from a notebook to production: pipelines, deployment, monitoring and the team habits around them.

For managers and leads: business cases, delivery, adoption and the approval gates that let AI ship safely.

Set up xAI's always on personal agent, connect your accounts and hand it real errands safely. Independent course, not affiliated with xAI.

Use Meta's personal AI agent across WhatsApp, Instagram and the web to plan, shop, research and stay on top of messages. Independent course, not affiliated with Meta.

Put OpenAI's Dots to work on recurring tasks: briefings, follow ups and research that run on a schedule. Independent course, not affiliated with OpenAI.

Learn the Model Context Protocol, the open standard that lets AI assistants and agents use your apps, files and data.

Build retrieval augmented generation so answers come from your documents, with sources, instead of from guesswork.

Package your know how as skills, folders of instructions and scripts that AI agents load when a task needs them.

Coordinate many agents and tools in one workflow: planners, workers, handoffs, retries and a human in the loop.

Give AI the right information at the right time: instructions, memory, retrieved data and tool results, within budget.

Go from an idea to a working web app by describing it to AI coding tools, with no coding background needed.

Measure whether an agent actually does the job: test sets, graders, traces and the checks to run before every release.

Everything in one cohort: agent design, tools and MCP, RAG, skills, orchestration, evals and running agents in production.