CRISP-DM Methodology for AI Project Delivery Training Course

Course overview

AI initiatives often stall between an enthusiastic proof of concept and a dependable business deployment. Teams may begin with a model, dataset or vendor tool before agreeing the decision to improve, the baseline to beat, the data required, or the controls needed for responsible use. This creates rework, unclear ownership, weak acceptance criteria and difficulty explaining whether a solution has delivered value. CRISP-DM provides a practical, repeatable structure for taking an AI use case from business question through data preparation, modelling, evaluation and deployment planning.

This five-day course applies the six CRISP-DM phases to AI project delivery: Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation and Deployment. Participants learn to frame measurable AI objectives, define success criteria and constraints, assess data readiness, document feature and data preparation decisions, select suitable modelling experiments, evaluate performance against business and risk criteria, and plan operational deployment. The course also addresses AI-specific concerns including human oversight, model explainability, bias checks, model monitoring and version-controlled experiment evidence.

Teaching combines instructor-led walkthroughs, worked AI delivery examples, group critiques and practical workshops using a realistic predictive AI case. Participants create a CRISP-DM AI project delivery pack containing a business objective statement, stakeholder map, data audit, modelling plan, evaluation scorecard, deployment roadmap and monitoring approach. This is a usable template set that can be adapted to an active project immediately after the course.

The course is designed for professionals who already contribute to digital, data, analytics or AI initiatives and need a disciplined delivery method that connects technical work with accountable business outcomes. It is particularly valuable where cross-functional teams must make sound go/no-go decisions on AI investments.

Course objectives

By the end of this course, participants will be able to:

• Frame an AI use case using a CRISP-DM Business Understanding charter with measurable objectives, assumptions and constraints
• Define business success criteria, model acceptance thresholds and decision rights for an AI project
• Assess dataset fitness using a data inventory, quality profile, lineage record and data-readiness checklist
• Create a Data Preparation plan covering feature definitions, sampling, transformation, leakage prevention and reproducibility
• Design a modelling experiment plan with baselines, validation strategy, performance metrics and experiment tracking
• Evaluate AI models using a CRISP-DM evaluation scorecard that combines technical, business, fairness and operational criteria
• Produce a deployment and monitoring plan covering model versioning, drift indicators, human review and retraining triggers
• Facilitate a CRISP-DM phase-gate review and present evidence-based go/no-go recommendations to stakeholders

Benefits of attending

Target competencies

Who should attend

• AI Project Managers — who need to govern delivery from business case through deployment and adoption
• Data Scientists — who need to connect modelling work to agreed business criteria, data controls and operational handover
• Data and Analytics Managers — who oversee portfolios of AI use cases and must prioritise investment using consistent evidence
• Business Analysts — who translate operational problems into measurable AI requirements and acceptance criteria
• Product Owners — who must define valuable AI-enabled product outcomes, user controls and release decisions
• Data Engineers — who need to plan reliable data preparation, lineage and production handover for AI solutions

Requirements and prerequisites

Participants should have experience contributing to a data, analytics, software or AI initiative and be comfortable discussing business requirements, datasets and project milestones. Familiarity with basic machine-learning terms such as training data, features, model, prediction, validation and accuracy is assumed. Participants should also be able to work with spreadsheets and interpret simple charts or tables. No programming, advanced statistics, prior use of JupyterLab, MLflow or Azure DevOps is required; demonstrations and guided templates are provided. This is not a course for building algorithms from first principles or learning Python.

Training methodology

The instructor uses a single realistic AI case throughout the week so participants can see how decisions made in Business Understanding affect data preparation, evaluation and deployment. Short teaching sessions introduce each CRISP-DM phase, followed by guided work in project teams using delivery templates, sample datasets and model evidence. Teams review one another’s assumptions at phase gates, practise sponsor-facing decisions, and receive instructor feedback on their artefacts. The final session converts the case outputs into an application plan for a participant’s own AI initiative.

Course outline

Day 1: Business Understanding and AI project framing

• CRISP-DM lifecycle, iteration loops and AI delivery governance
• Distinguishing automation, analytics, predictive AI and generative AI use cases
• Business problem statements, decision points and target users
• SMART business objectives and measurable value hypotheses
• Stakeholder mapping, decision rights and accountable project roles
• AI feasibility assumptions, constraints and dependency mapping
• Business success criteria, model success criteria and phase-gate definitions

Workshop: Participants develop a Business Understanding charter for a predictive AI case, including value hypothesis, stakeholders, constraints and initial success measures.

Day 2: Data Understanding and preparation planning

• Data source inventories, ownership and access pathways
• Data profiling for completeness, validity, timeliness and consistency
• Target-variable definition and label-quality assessment
• Data lineage, provenance and documentation for AI delivery
• Bias risks, representativeness and protected-characteristic considerations
• Feature definitions, data transformations and leakage prevention
• Training, validation and test dataset partitioning

Workshop: Participants complete a data-readiness assessment and produce a preparation plan with data risks, feature decisions and validation splits.

Day 3: Modelling strategy and experiment management

• Selecting model approaches based on decision context and data characteristics
• Baseline models and the value of simple comparator methods
• Hypothesis-led experiment design and modelling backlogs
• Cross-validation, holdout testing and reproducible experiment settings
• Classification, regression and ranking performance measures
• JupyterLab notebooks for transparent analysis and experiment evidence
• MLflow runs, parameters, metrics and model artefact tracking

Workshop: Participants create a modelling experiment plan and record baseline-versus-candidate results in an MLflow-style experiment log.

Day 4: Evaluation, risk review and release decisions

• CRISP-DM Evaluation phase and independent business validation
• Confusion matrices, threshold selection and cost-sensitive performance
• Segment-level testing for bias, stability and unequal error rates
• Explainability evidence, user trust and challenge procedures
• Privacy, security and responsible AI control considerations
• Evaluation scorecards combining business, technical and operational criteria
• Go, revise, pilot and stop decisions at the AI phase gate

Workshop: Participants conduct a phase-gate evaluation review, score a model against acceptance criteria and present a go/no-go recommendation.

Day 5: Deployment, monitoring and CRISP-DM application

• Deployment patterns for batch, real-time and human-in-the-loop AI
• Operational acceptance criteria and production handover requirements
• Model registry, version control and approval traceability
• Performance monitoring, data drift and concept drift indicators
• Feedback loops, incident handling and retraining triggers
• Azure DevOps Boards for delivery tasks, risks and phase-gate actions
• CRISP-DM iteration planning and portfolio-level lessons learned

Workshop: Participants assemble and present a complete CRISP-DM AI delivery pack and create a 30-day application plan for an active workplace initiative.

Tools & standards covered

CRISP-DM 1.0, JupyterLab, MLflow, Azure DevOps Boards

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions