Module 7: AI Development Lifecycle

Learning Content

Module Overview

The AI Development Lifecycle encompasses all stages from ideation through production operation. Unlike traditional software development, AI systems require unique processes for data management, experimentation tracking, model versioning, and continuous monitoring. Understanding this lifecycle helps Malta businesses build sustainable AI capabilities that deliver long-term value.

The Complete AI Development Lifecycle

Stage 1: Problem Definition & Feasibility

Activities: Identify business problem, define success metrics, assess AI suitability, estimate ROI

Key Questions:

• Is this problem actually solvable with AI? (pattern-based, data-rich, tolerates imperfection)
• Do we have sufficient data? (minimum 1,000-10,000 labeled examples for supervised learning)
• What's the business value if successful? (quantify in revenue/cost impact)
• What accuracy is "good enough"? (80%? 90%? 95%?)

Deliverables: Problem definition document, feasibility assessment, business case with ROI projection

Stage 2: Data Collection & Preparation

Activities: Gather data, clean and validate, label for supervised learning, create train/test splits, build data pipelines

Critical Steps:

• Data Profiling: Understand data distributions, missing rates, outliers, correlations
• Data Cleaning: Handle missing values, fix errors, remove duplicates, standardize formats
• Feature Engineering: Transform raw data into meaningful features for ML (e.g., "days since last purchase" from transaction dates)
• Data Splits: Train (60-70%), Validation (15-20%), Test (15-20%) to prevent overfitting
• Versioning: Track dataset versions (like Git for code, but for data)—tools: DVC, Pachyderm

Time Allocation: 40-60% of total project time typically spent here

Stage 3: Model Development & Experimentation

Activities: Select algorithms, train models, tune hyperparameters, evaluate performance, iterate based on results

Experimentation Process:

1. Baseline Model: Start with simplest approach (logistic regression, decision tree) to establish baseline performance
2. Experiment Tracking: Log every experiment (algorithm, features, hyperparameters, results) using MLflow, Weights & Biases, or spreadsheets
3. Iterative Improvement: Try different algorithms, features, and hyperparameters systematically
4. Model Selection: Choose model balancing accuracy, interpretability, and computational cost

Common Pitfall: Overfitting—model performs great on training data but poorly on new data. Prevent with validation sets and cross-validation.

Stage 4: Model Evaluation & Validation

Activities: Test model on holdout data, assess business metrics, validate with domain experts, identify failure modes

Evaluation Dimensions:

• Technical Metrics: Accuracy, precision, recall, F1 score, AUC-ROC (depends on problem type)
• Business Metrics: ROI, cost savings, revenue impact, user satisfaction
• Fairness & Bias: Does model perform equally across demographic groups? (GDPR/ethical requirement)
• Explainability: Can we explain why model made specific predictions? (Critical for MGA/MFSA compliance)
• Robustness: Does model handle edge cases and data distribution shifts gracefully?

Stage 5: Deployment & Integration

Activities: Package model, build serving infrastructure, integrate with business systems, conduct user acceptance testing

Deployment Patterns:

• Batch Predictions: Run model on schedule (daily, hourly) to score large datasets. Example: Daily churn risk scoring for all players.
• Real-Time API: Serve predictions on-demand via REST API. Example: Fraud detection for each transaction as it happens.
• Edge Deployment: Run model on devices (mobile, IoT). Example: Recommendation engine in mobile app.

Rollout Strategy: Start with shadow mode (predictions made but not acted upon), then limited rollout (10% of traffic), finally full deployment

Stage 6: Monitoring & Maintenance

Activities: Monitor performance, detect model drift, retrain periodically, handle production incidents

Key Monitoring Metrics:

• Model Performance: Is accuracy maintaining target levels? Track daily/weekly.
• Data Drift: Is input data distribution changing? (New customer types, seasonal patterns)
• Prediction Drift: Is model's output distribution changing?
• System Health: Latency, throughput, error rates, uptime

Model Retraining Triggers: Performance drop below threshold, significant data drift detected, quarterly scheduled retraining, or major business changes

MLOps: Operationalizing the Lifecycle

Version Control for ML

• Code: Git for model training code, deployment code, data pipelines
• Data: DVC or Pachyderm for dataset versioning (like Git for large data files)
• Models: MLflow Model Registry or similar for trained model versioning
• Experiments: Track hyperparameters, metrics, and artifacts for every training run

CI/CD for ML

Continuous Integration: Automated testing when code changes

• Unit tests for data processing code
• Data validation tests (schema checks, distribution checks)
• Model performance tests (accuracy above threshold on test set)

Continuous Deployment: Automated deployment when tests pass

• Package model in container (Docker)
• Deploy to staging environment for UAT
• Automated deployment to production if staging tests pass
• Automated rollback if production metrics degrade

Key Takeaways

• AI development lifecycle has 6 stages: Problem Definition, Data Preparation, Model Development, Evaluation, Deployment, and Monitoring
• Data preparation consumes 40-60% of project time—don't underestimate this phase
• MLOps brings DevOps principles to ML: version control (code, data, models), CI/CD, monitoring, automation
• Always start with baseline model (simple algorithm) before complex approaches—establishes performance floor
• Model performance degrades over time (drift)—plan for continuous monitoring and periodic retraining
• For Malta businesses: Use platforms (like MAIA) that handle MLOps complexity if lacking dedicated ML engineering team
• Track experiments systematically (MLflow, spreadsheets)—what you don't document, you'll forget and repeat
• Deploy gradually: shadow mode → limited rollout → full deployment (de-risks production issues)