The Machine Learning Workflow
End-to-End Process
1. Problem Definition
2. Data Collection
3. Data Preparation
4. Model Selection
5. Model Training
6. Model Evaluation
7. Deployment
8. Monitoring & Maintenance
Step 1: Problem Definition
Framing the Business Challenge
Key Questions to Answer
- What is the business problem?
- Is ML the right solution?
- What does success look like?
- What are the constraints?
- Who are the stakeholders?
Define Clear Objectives
- Business Goal: Increase revenue, reduce costs
- ML Task: Classification, regression, clustering
- Success Metrics: Accuracy, ROI, user satisfaction
- Constraints: Time, budget, resources
- Deliverables: Model, API, dashboard
Common Mistake: Starting with a solution in mind rather than understanding the problem.
"We need deep learning!" vs "We need to reduce customer churn by 20%"
Step 2: Data Collection
Gathering Your Raw Materials
Data Sources
- Internal: Databases, logs, CRM systems
- External: APIs, public datasets, purchases
- Generated: Surveys, experiments, sensors
- Third-party: Data vendors, partnerships
Quality Considerations
- Relevance: Does it help solve the problem?
- Accuracy: How reliable is the source?
- Completeness: Are there gaps?
- Timeliness: Is it current enough?
- Volume: Do we have enough data?
Legal & Ethical: Always verify data usage rights, privacy compliance (GDPR, CCPA),
and consider ethical implications of data collection.
Step 3: Data Preparation
Transforming Raw Data into ML-Ready Format
Data Cleaning
- Handle missing values (drop, impute, flag)
- Remove duplicates
- Fix inconsistencies
- Detect and handle outliers
- Correct data types
Feature Engineering
- Create new features from existing ones
- Encode categorical variables
- Scale/normalize numerical features
- Extract temporal features
- Combine features (interactions, polynomials)
# Example: Data preparation
df['age_group'] = pd.cut(df['age'], bins=[0, 25, 40, 60, 100])
df['log_income'] = np.log1p(df['income'])
df = pd.get_dummies(df, columns=['category'])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
Time Investment: Expect to spend 60-80% of your project time on data preparation.
Quality data preparation is the foundation of successful ML models.
Step 4: Model Selection
Choosing the Right Algorithm
Selection Criteria
- Problem Type: Classification vs regression
- Data Size: Small vs big data
- Interpretability: Black box vs explainable
- Training Time: Real-time vs batch
- Accuracy Requirements: Good enough vs state-of-art
Common Starting Points
- Linear Models: Simple, fast, interpretable
- Tree-Based: Handle non-linearity well
- Ensemble Methods: Often best accuracy
- Neural Networks: Complex patterns, lots of data
- SVM: High-dimensional data
Best Practice: Start simple! Begin with logistic/linear regression as a baseline,
then try more complex models only if needed. Simple models are easier to deploy and maintain.
Step 5: Model Training
Teaching the Algorithm to Learn
Training Process
- Initialize model with default parameters
- Feed training data to algorithm
- Algorithm learns patterns
- Adjust hyperparameters
- Use validation set for tuning
Hyperparameter Tuning
- Grid Search: Try all combinations
- Random Search: Sample randomly
- Bayesian Optimization: Smart search
- Cross-Validation: Robust evaluation
- Early Stopping: Prevent overfitting
# Example: Model training with hyperparameter tuning
from sklearn.model_selection import GridSearchCV
param_grid = {'max_depth': [3, 5, 7], 'min_samples_split': [2, 5, 10]}
grid_search = GridSearchCV(DecisionTreeClassifier(), param_grid, cv=5)
grid_search.fit(X_train, y_train)
best_model = grid_search.best_estimator_
Step 6: Model Evaluation
Measuring Performance and Validity
Evaluation Strategy
- Use test set (never seen during training)
- Calculate relevant metrics
- Compare to baseline/benchmark
- Check for overfitting/underfitting
- Validate business assumptions
Key Metrics by Type
Regression:
- RMSE, MAE, MAPE
- R², Adjusted R²
Classification:
- Accuracy, Precision, Recall
- F1-Score, ROC-AUC
- Confusion Matrix
Business Context: A 95% accurate model isn't useful if the 5% errors are your most valuable customers.
Always evaluate in business context, not just statistical metrics.
Step 7: Deployment
Putting Models into Production
Deployment Options
- Batch: Periodic predictions
- Real-time: API endpoints
- Edge: On-device deployment
- Embedded: Within applications
- Cloud: Scalable services
Production Considerations
- Scalability: Handle production load
- Latency: Response time requirements
- Integration: Fit into existing systems
- Versioning: Track model versions
- Rollback: Plan for failures
MLOps: Modern deployment uses MLOps practices - automated pipelines, containerization (Docker),
orchestration (Kubernetes), and model serving platforms (TensorFlow Serving, MLflow).
Step 8: Monitoring & Maintenance
Keeping Models Healthy in Production
What to Monitor
- Performance Metrics: Accuracy over time
- Data Drift: Input distribution changes
- Concept Drift: Relationship changes
- System Health: Latency, errors, uptime
- Business Impact: ROI, user satisfaction
Maintenance Actions
- Retraining: Update with new data
- Recalibration: Adjust thresholds
- A/B Testing: Compare versions
- Feature Updates: Add/remove features
- Model Replacement: Switch algorithms
Model Decay: All models degrade over time as patterns change.
Without monitoring and maintenance, a great model can become harmful to business.