PMI Certified Professional in Managing AI (PMI-CPMAI™)

Turn raw data into clean, reliable, AI-ready fuel for high-performing models.

1. Why Data Engineering Is the Backbone of AIUnderstand why clean, well-structured data is essential, and how robust data engineering keeps it flowing from source to model.

2. Building Automated Data PipelinesDesign scalable pipelines that collect, ingest, and prepare data for both model training and real-time inference.

3. Cleansing and Enhancing DataCorrect errors, remove duplicates, and enrich data with new features to boost model accuracy and reliability.

4. Sampling and Splitting DatasetsSelect the right data and divide it into training, validation, and test sets for dependable, unbiased results.

5. Expanding Data with Augmentation and Synthetic DataOvercome limited data by generating new, realistic examples that strengthen model performance.

6. Labelling Data at ScaleCompare labelling approaches, from in-house teams to crowdsourcing and human-in-the-loop review, to create high-quality training data.

7. Using Generative AI for Data PreparationDiscover how Generative AI can accelerate data augmentation and curate diverse, representative datasets.

8. Governing Data Across Its LifecycleEstablish stewardship roles, security controls, and compliance practices that keep prepared data trustworthy over time.

Build, train, and refine AI models through fast, iterative development.

1. Machine Learning EssentialsUnderstand supervised, unsupervised, and reinforcement learning, and when each delivers the best business results.

2. Why AI Development Must Be IterativeLearn how continuous testing, feedback, and refinement turn early models into high-performing AI systems.

3. Choosing the Right AlgorithmExplore popular algorithms, from regression and decision trees to neural networks, and match them to your business problem.

4. Developing and Tuning AI ModelsDiscover how training, hyperparameter tuning, and ensemble methods work together to maximize model performance.

5. Accelerating Development with AutoML and Transfer LearningReduce time and cost by automating model building and reusing proven third-party models.

6. Validating Models for Real-World AccuracyEnsure your model generalizes to new data by mastering the balance between overfitting and underfitting.

7. Building Generative AI SystemsExamine how large language models are turned into practical, business-ready Generative AI solutions.

8. Enhancing LLMs with RAG and Fine-TuningUse retrieval-augmented generation to ground responses in your own data, and fine-tune models for your business needs.

Prove your AI works, stays reliable, and delivers real business value.

1. Why Model Evaluation MattersLearn how rigorous evaluation exposes hidden weaknesses before they reach your customers or your bottom line.

2. Testing AI Systems End to EndApply unit, integration, end-to-end, and stress testing to build robust systems that handle unexpected inputs.

3. Measuring What MattersEvaluate models with accuracy, precision, recall, and F1 score to confirm they hit business and technical targets.

4. Detecting Data and Model DriftRecognize early signs of performance decline, and learn how to measure, respond, and maintain ongoing oversight.

5. Knowing When to RetrainPlan the timing and process of model retraining so results stay accurate as real-world conditions change.

6. Optimizing Resource UseAchieve the right balance between model performance and computing cost for efficient, scalable AI.

7. Ensuring Auditability and TraceabilityBuild reliable audit trails and integrate with DevSecOps for accountable, compliant AI.

8. Making AI Transparent and InterpretableUnderstand the limits of explainability and discover how interpretable AI earns stakeholder trust.

Take AI from the lab to the real world and keep it delivering value.

1. Moving Models into ProductionLearn what changes when AI shifts from training to real-world inference, from infrastructure to data pipelines.

2. Choosing the Right AI InfrastructureUnderstand the four technology environments that support AI from development and training to deployment.

3. Deciding How and Where to DeployCompare batch, real-time, and streaming predictions across cloud, edge, and on-premises options.

4. Running Generative AI in ProductionEvaluate self-hosted versus API-hosted models and manage real risks like hallucinations and jailbreaks.

5. Managing the Continuous AI Lifecycle with MLOpsDiscover why AI lifecycle are continuous, and how DevOps and MLOps keep models current and effective.

6. Governing Models in OperationImplement a governance framework and ongoing monitoring that hold every deployed model accountable.

7. Securing AI Against ThreatsProtect models from malicious attacks while staying ahead of emerging AI regulations.

8. Recognizing the Limits of AIUnderstand what AI can and cannot do and plan the next iteration for continuous improvement.