Progressive pathway
Each level prepares learners to take on increasingly complex professional responsibilities.
Professional academy
Academy of artificial intelligence
Develop the competencies required to understand, build, integrate, operate, and govern artificial intelligence solutions in enterprise environments.
Academic approach
Training designed to demonstrate real-world capabilities
The academy integrates foundations, specialization, engineering, architecture, labs, and competency-based assessment.
Applied learning
Content is delivered through business cases, guided practice, and projects.
Competency-based certification
Learners must demonstrate knowledge, practical execution, and the ability to apply what they have learned.
Vendor-neutral approach
The principles are transferable across tools and AI platforms.
Professional pathway
Build your profile from practitioner to architect
The progression allows learners to start with the foundations, specialize in a domain, and advance toward engineering and architecture responsibilities.
Predictive, generative, and agentic specializations can be taken independently before entering the engineering certification.
AI practitioner
Fundamentals, use cases, risks, ethics, and responsible adoption.
Predictive AI specialist
Predictive models, data preparation, and evaluation.
Generative AI specialist
Prompts, RAG, foundation models, and generative applications.
Agentic AI specialist
Agents, tools, memory, workflows, and orchestration.
AI engineer
Integration, deployment, observability, security, and MLOps.
AI architect
Enterprise architecture, governance, resilience, and scalability.
Learning plan
Six courses covering the artificial intelligence pathway
Each course can be taken independently or as part of a stackable certification.
Course 1
Artificial intelligence fundamentals
A comprehensive introduction to the concepts, applications, limitations, and risks of artificial intelligence.
- AI and machine learning fundamentals
- Business use cases
- Ethics, privacy, and responsible use
- Initial adoption project
Course 2
Machine learning and predictive analytics
Design, train, and evaluate predictive models for business problems.
- Data preparation and quality
- Supervised and unsupervised models
- Metrics and validation
- Applied predictive project
Course 3
Generative artificial intelligence engineering
Build generative applications using foundation models, RAG, tools, and evaluation.
- Instruction design
- Embeddings and semantic search
- RAG and vector databases
- Response evaluation and security
Course 4
Intelligent agents and cognitive automation
Design agents that can plan, use tools, maintain context, and execute tasks.
- Agent architecture
- Memory and context
- Tools and functions
- Multi-agent orchestration
Course 5
AI engineering and MLOps
Integrate, deploy, operate, monitor, and continuously improve artificial intelligence solutions.
- APIs and integration architecture
- Containers and deployment
- Monitoring and observability
- Security and lifecycle management
Course 6
Enterprise AI architecture and governance
Design scalable, secure, governed enterprise platforms aligned with strategy.
- Architecture patterns
- Governance, risk, and compliance
- AI security
- Enterprise architecture project
Professional credentials
Six certifications aligned with professional roles
Specialist credentials can be earned independently. Engineering and architecture credentials are cumulative.
AI practitioner
Validates fundamental knowledge, adoption criteria, and responsible use of artificial intelligence.
Requires: Course 1 and a scenario-based exam.
Predictive AI specialist
Demonstrates the ability to prepare data, select models, and evaluate predictive solutions.
Requires: Course 2 and a practical lab.
Generative AI specialist
Validates the design of generative applications with RAG, evaluation, security, and information retrieval.
Requires: Course 3 and a practical lab.
Agentic AI specialist
Demonstrates the ability to build agents, tools, memory, and intelligent workflows.
Requires: Course 4 and an applied project.
AI engineer
Validates AI solution integration, deployment, operations, observability, security, and maintenance.
Requires: Courses 2, 3, 4, and 5 and a project.
AI architect
Certifies the ability to design scalable, secure, governed enterprise architectures.
Requires: The complete pathway and a final project defense.
Technology ecosystem
Tools and platforms used during training
The academy takes a vendor-neutral approach and uses current technologies to develop practical skills.
Python
OpenAI
Microsoft Azure
AWS
Hugging Face
LangChain
LlamaIndex
MLflow
Docker
GitHub
Vector databases
Hands-on learning
Labs and projects that demonstrate competency
Activities can be adapted to each organization or cohort.
Demand forecasting
Prepare data, train, evaluate, and explain a predictive model.
Enterprise assistant with RAG
Build a solution that searches documents, retrieves context, and generates verifiable answers.
Operations agent
Create an agent that uses tools, executes actions, and records results.
Artificial intelligence API
Expose an AI model or service through a secure, documented API.
AI solution monitoring
Configure metrics, traceability, evaluation, and operational alerts.
Enterprise architecture
Design a comprehensive solution with components, security, governance, and cost estimates.
Capstone project
Design a complete enterprise artificial intelligence solution
The final project brings together strategy, architecture, data, development, evaluation, security, operations, and governance.
Request project information1. Assess and define the use case
2. Design functionality and architecture
3. Develop the prototype
4. Evaluate technical and business outcomes
5. Present and defend the project
Entry profile
- Professionals in technology, data, automation, innovation, or digital transformation.
- Analysts, developers, consultants, educators, and process managers.
- Basic knowledge of logic, data, and digital tools.
- Prior technical experience is recommended for engineering courses.
Graduate profile
- AI practitioner, AI specialist, AI engineer, or AI architect.
- Developer of generative and agentic solutions.
- Consultant in AI adoption and transformation.
- Responsible for AI architecture, operations, risk, or governance.
Frequently asked questions
Key information before you start
No. Each course can be taken independently. The full pathway is required only for the AI Architect certification.
Course participation and certification are managed separately. You can complete training without taking the exam or apply for certification later.
Introductory courses do not require advanced programming. Predictive, engineering, and MLOps pathways include technical activities and benefit from basic Python skills.
The approach is vendor-neutral. Labs may use different tools to develop skills that transfer across technology ecosystems.
Yes. The academy can be tailored to an organization's roles, use cases, policies, technologies, and projects.
Start your professional pathway in artificial intelligence
Request information about upcoming cohorts, delivery options, pricing, certifications, and corporate programs.