Architect AI Solutions: From Needs to Models Course

Lesson 1 • Key Roles in AI Projects

Maps stakeholder roles—architect, data scientist, engineer, sponsor—and their responsibilities. Clarifies where the architect role sits within the team.

Lesson 2 • AI Readiness Assessment

Evaluates organisational data maturity, infrastructure, and talent before committing to an AI initiative. Prevents misaligned project scoping.

Lesson 3 • What AI Solutions Actually Are

Defines AI solutions by their functional components and distinguishes them from traditional software. Anchors all subsequent architecture decisions in this taxonomy.

Lesson 1 • Feasibility and Value Assessment

Evaluates technical feasibility, data availability, and expected business value before design begins. Justifies investment decisions to sponsors.

Lesson 2 • Problem Framing and Scoping

Transforms business pain points into bounded, measurable AI problem statements. Prevents scope creep and misaligned model objectives.

Lesson 3 • Documenting the AI Problem Brief

Produces a structured artefact capturing problem context, constraints, success criteria, and stakeholder sign-off. Serves as the contract for solution design.

Lesson 1 • Data Quality Assessment

Applies completeness, accuracy, consistency, and timeliness dimensions to evaluate dataset fitness. Directly determines model reliability.

Lesson 2 • Data Sourcing and Acquisition

Identifies internal, external, and synthetic data sources aligned to the problem brief. Establishes acquisition pipelines and licensing considerations.

Lesson 3 • Data Pipelines and Engineering

Designs ingestion, transformation, and storage pipelines that feed models consistently. Connects raw data sources to feature-ready datasets.

Lesson 4 • Labelling and Annotation Strategy

Plans human and automated labelling workflows for supervised learning datasets. Balances cost, speed, and label quality.

Lesson 1 • Model Family Taxonomy

Surveys regression, classification, clustering, generative, and reinforcement model families. Provides the selection vocabulary used throughout the chapter.

Lesson 2 • Feature Engineering Principles

Transforms raw data into informative features that improve model performance. Bridges the data strategy chapter with model training.

Lesson 3 • Architecture Design Patterns

Introduces ensemble, pipeline, and multi-model architecture patterns for complex problems. Guides structural decisions before implementation begins.

Lesson 4 • Model Selection Criteria

Applies interpretability, latency, data volume, and accuracy trade-offs to narrow model choices. Links selection criteria back to the problem brief.

Lesson 1 • Model Validation and Testing

Applies cross-validation, holdout testing, and adversarial probing to confirm model generalisation. Produces the validation report used in deployment decisions.

Lesson 2 • Training Infrastructure Choices

Evaluates local, cloud, and distributed training environments against cost, scale, and latency needs. Informs infrastructure provisioning decisions.

Lesson 3 • Handling Imbalance and Bias in Training

Addresses class imbalance, sampling bias, and representation gaps during training. Connects ethical foundations to practical training decisions.

Lesson 4 • Experiment Design and Tracking

Structures training experiments with controlled variables and logs all parameters, metrics, and artefacts. Enables reproducibility and comparison.

Lesson 1 • Security and Access Control in Deployment

Applies authentication, authorisation, and data encryption to deployed model endpoints. Protects models and inference data from unauthorised access.

Lesson 2 • CI/CD for Machine Learning

Extends continuous integration and delivery pipelines to include model training, validation, and release gates. Automates safe model promotion.

Lesson 3 • Deployment Readiness Review

Conducts a structured pre-launch checklist covering performance, security, compliance, and rollback readiness. Gates production release on verified criteria.

Lesson 4 • Deployment Architecture Patterns

Compares batch inference, real-time API, edge, and embedded deployment patterns. Matches each pattern to problem latency and scale requirements.

Lesson 1 • Model Governance and Audit Trails

Maintains versioned records of model lineage, decisions, and changes for regulatory and internal audit purposes. Supports accountability requirements.

Lesson 2 • Drift Detection and Root Cause Analysis

Distinguishes data drift, concept drift, and upstream pipeline failures as degradation causes. Guides targeted remediation rather than full retraining.

Lesson 3 • Production Monitoring Fundamentals

Tracks prediction quality, data drift, and system health metrics in real time. Provides early warning before model degradation affects business outcomes.

Lesson 4 • Retraining and Model Refresh Strategies

Designs scheduled, triggered, and continuous retraining workflows to keep models current. Balances retraining cost against performance decay risk.

Lesson 1 • AI Strategy Roadmap Development

Synthesises architecture, governance, and portfolio decisions into a multi-year AI strategy roadmap. Serves as the capstone deliverable for the core curriculum.

Lesson 2 • Measuring AI Business Impact

Defines KPIs, attribution models, and reporting cadences to quantify AI's contribution to business outcomes. Justifies continued investment to executive stakeholders.

Lesson 3 • Enterprise AI Architecture Patterns

Surveys centralised, federated, and hybrid AI platform architectures at enterprise scale. Guides platform decisions that support multiple teams and use cases.

Lesson 4 • AI Governance Framework Design

Establishes policies, review boards, and accountability structures for responsible AI at scale. Operationalises the ethical foundations from Chapter 1.