Building a machine learning system is more than just training a model. It’s a multi-phase process that requires careful planning, data preparation, iterative experimentation, and ongoing maintenance. Understanding these phases is essential for successful ML projects.
ML projects are iterative, not linear. You’ll often revisit earlier phases based on what you learn in later phases. For example, poor model performance might send you back to collect better data or refine the problem definition.
Data Collection & Preparation
(Iterate based on feedback)
Before writing any code, clearly define what you’re solving.
Question Why It Matters What problem are we solving? Ensures everyone aligns on goals What does success look like? Defines metrics and targets Is ML the right solution? Avoids overcomplicating simple problems What are the constraints? Time, budget, data, compute resources Who are the stakeholders? Understanding users and requirements
Type Examples Business metrics Revenue increase, cost reduction, customer satisfaction ML metrics Accuracy, F1 score, AUC, MAE, calibration System metrics Latency, throughput, availability, error rate
Pitfall Consequence Prevention Solving the wrong problem Wasted effort Stakeholder interviews, clarify objectives No clear success criteria Can’t measure success Define metrics upfront ML is unnecessary complexity Overengineered solution Consider simpler alternatives first
Deliverable : Problem statement with clear success criteria and constraints.
Data is the foundation of ML—this phase often takes the majority of project time.
Consideration Questions Data sources Internal databases? External APIs? Third-party data? Data quality Is it accurate? Complete? Representative? Data quantity Do we have enough samples? Legal/ethical Do we have rights to use this data? Privacy concerns?
Activity Purpose Summary statistics Understand distributions, outliers Visualization Spot patterns, relationships Missing value analysis Identify data quality issues Correlation analysis Find feature relationships
Step Description Cleaning Handle missing values, remove duplicates, fix errors Integration Combine data from multiple sources Transformation Normalize, scale, encode categorical variables Reduction Feature selection, dimensionality reduction Splitting Train/validation/test split
Pitfall Consequence Prevention Data leakage Overly optimistic results Keep test data completely separate Poor train/test split Biased evaluation Use stratified splits, temporal splits for time series Ignoring data quality Garbage in, garbage out Rigorous EDA, data validation
Deliverable : Versioned dataset snapshot + documented preprocessing steps + reproducible pipeline.
This is the experimentation phase where you build and iterate on models.
Consideration Options Problem type Classification, regression, clustering, etc. Data characteristics Structured vs. unstructured, size, quality Interpretability Need to explain decisions? Latency Real-time or batch? Resources Compute, time, expertise
Technique When to Use One-hot encoding Categorical variables Binning Continuous to categorical Polynomial features Capture non-linear relationships Interaction terms Feature combinations Domain-specific features Leveraging expert knowledge
Iterate (features, hyperparameters, algorithm)
Pitfall Consequence Prevention Starting too complex Wasted time, overfitting Start with simple baselines Overfitting training data Poor generalization Cross-validation, regularization Ignoring baselines Don’t know if ML helps Compare against simple rules Not documenting experiments Can’t reproduce results Use experiment tracking (MLflow, Weights & Biases)
Deliverable : Trained model with documented performance and experimentation results.
Before deployment, thoroughly validate your model.
Technique When to Use K-fold cross-validation Limited data, reliable estimate Stratified K-fold Imbalanced classes Time series split Temporal data (no future leakage) Hold-out set Large datasets
Analysis What It Reveals Confusion matrix Classification errors by type ROC/PR curves Tradeoffs for binary classification (PR often better for imbalance) Residual plots Regression error patterns Feature importance Which features drive predictions (when applicable) Error analysis Specific failure modes
Test Type Purpose Holdout test set Generalization to unseen data Load testing Latency and throughput under traffic Input robustness testing Behavior with edge cases, missing fields, outliers Adversarial testing Security vulnerabilities
Pitfall Consequence Prevention Only reporting average metrics Hides edge cases Report metrics by segment, analyze errors Testing on training data Overconfident results Strict train/validation/test separation Ignoring confidence intervals Misleading performance Report uncertainty Not testing edge cases Production failures Test with rare/imperfect inputs
Deliverable : Comprehensive evaluation report with performance metrics, error analysis, and known limitations.
Putting your model into production.
Consideration Options Deployment type Cloud, on-premises, edge Serving pattern Batch, real-time, streaming Scalability Handle expected load Latency Meet application requirements Monitoring Track performance in production
Pattern Use Case Batch inference Daily reports, recommendations Real-time API Interactive applications Stream processing Real-time monitoring Edge deployment Mobile, IoT devices A/B testing / canary Gradual rollout, online evaluation
Component Purpose Model serving TensorFlow Serving, TorchServe, SageMaker, KServe API gateway REST/gRPC endpoints Load balancer Distribute traffic Feature store Consistent feature computation Monitoring Performance, drift, errors
Pitfall Consequence Prevention Poor error handling Cascading failures Graceful degradation, fallbacks Not versioning models Can’t rollback Model registry, CI/CD Ignoring latency Poor UX Load test, optimize inference No monitoring Blind to issues Comprehensive observability
Deliverable : Deployed model with serving infrastructure, monitoring, and rollback plan.
ML models degrade over time—continuous monitoring is essential.
Metric Type Examples Performance Accuracy, F1 (when labels available, often delayed), latency, throughput Data drift Feature distribution changes Output drift Prediction distribution changes System health CPU, memory, errors, availability Business metrics User engagement, conversion rates
Type What It Is Detection Covariate drift Input distribution changes Statistical tests on features Label drift Target distribution changes Track label frequencies Concept drift Relationship changes Monitor prediction quality over time
Strategy When to Use Scheduled retraining Predictable data patterns Trigger-based retraining Performance drops below threshold Online learning Continuous model updates Active learning Human-in-the-loop for uncertain cases
Pitfall Consequence Prevention No monitoring Silent failures Comprehensive metrics, alerting Ignoring drift Degrading performance Regular drift analysis, retraining Not documenting changes Can’t understand evolution Change logs, version control Manual processes Errors, slow Automate MLOps pipelines
Deliverable : Monitoring dashboard, alerting system, documented retraining procedures.
Phase #1 Mistake #2 Mistake #3 Mistake Problem Definition Wrong problem No success criteria Ignoring constraints Data Data leakage Poor quality Insufficient exploration Model Development Overfitting No baseline Poor experimentation Evaluation Testing on train data Only average metrics Missing edge cases Deployment No monitoring Poor error handling Lack of scalability Maintenance Ignoring drift No retraining plan Manual processes
ML projects have six main phases:
Problem Definition : Clearly define the problem and success criteriaData Collection & Preparation : Gather, clean, and prepare quality dataModel Development : Experiment with features and algorithms iterativelyEvaluation & Validation : Thoroughly test and analyze model performanceDeployment : Put the model into production with proper infrastructureMonitoring & Maintenance : Continuously monitor and retrain as needed
Key principles :
It’s iterative, not linear—expect to revisit phases
Data preparation often takes the most time
Start simple, then add complexity
Always establish baselines before trying ML
Deployment is just the beginning—monitoring is ongoing