Introduction

MLOps Fundamentals

The ML Lifecycle

Understanding the end-to-end machine learning process:

ML Lifecycle Stages:

• Problem definition and scoping
• Data collection and preparation
• Feature engineering and selection
• Model development and training
• Model evaluation and validation
• Model deployment and serving
• Monitoring and maintenance
• Continuous improvement

MLOps vs. Traditional DevOps:

• Data and model versioning (not just code)
• Experiment tracking and reproducibility
• Model-specific testing requirements
• Specialized deployment patterns
• Performance monitoring beyond uptime
• Retraining workflows

MLOps Maturity Levels:

• Level 0: Manual process, no automation
• Level 1: ML pipeline automation, CI/CD
• Level 2: Automated retraining pipeline
• Level 3: Full automation with governance

Example MLOps Workflow:

┌───────────────┐     ┌───────────────┐     ┌───────────────┐
│               │     │               │     │               │
│  Data         │────▶│  Model        │────▶│  Model        │
│  Pipeline     │     │  Development  │     │  Deployment   │
│               │     │               │     │               │
└───────┬───────┘     └───────┬───────┘     └───────┬───────┘
        │                     │                     │
        │                     │                     │
        ▼                     ▼                     ▼
┌───────────────┐     ┌───────────────┐     ┌───────────────┐
│               │     │               │     │               │
│  Feature      │     │  Experiment   │     │  Model        │
│  Store        │     │  Tracking     │     │  Registry     │
│               │     │               │     │               │
└───────────────┘     └───────────────┘     └───────┬───────┘
                                                    │
                                                    │
                                                    ▼
┌───────────────┐     ┌───────────────┐     ┌───────────────┐
│               │     │               │     │               │
│  Model        │◀────│  Model        │◀────│  Model        │
│  Retraining   │     │  Monitoring   │     │  Serving      │
│               │     │               │     │               │
└───────────────┘     └───────────────┘     └───────────────┘

Cross-Functional Collaboration

Bridging the gap between data science and engineering:

Key Roles in MLOps:

• Data Scientists
• ML Engineers
• DevOps Engineers
• Data Engineers
• Platform Engineers
• Product Managers

Collaboration Challenges:

• Different toolsets and workflows
• Knowledge gaps between disciplines
• Handoff friction between teams
• Conflicting priorities and timelines
• Shared responsibility boundaries

Collaboration Best Practices:

• Establish common terminology
• Define clear handoff processes
• Create shared documentation
• Implement collaborative tools
• Conduct cross-training sessions
• Form cross-functional teams

Model Development and Training

Experiment Management

Tracking and organizing ML experiments:

Experiment Tracking Components:

• Code versioning
• Data versioning
• Parameter tracking
• Metrics logging
• Artifact management
• Environment capture

Example MLflow Tracking:

# MLflow experiment tracking example
import mlflow
import mlflow.sklearn
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

# Set experiment
mlflow.set_experiment("customer_churn_prediction")

# Start run
with mlflow.start_run(run_name="random_forest_baseline"):
    # Log parameters
    params = {
        "n_estimators": 100,
        "max_depth": 10,
        "min_samples_split": 5,
        "random_state": 42
    }
    mlflow.log_params(params)
    
    # Train model
    model = RandomForestClassifier(**params)
    model.fit(X_train, y_train)
    
    # Make predictions
    y_pred = model.predict(X_test)
    
    # Log metrics
    metrics = {
        "accuracy": accuracy_score(y_test, y_pred),
        "precision": precision_score(y_test, y_pred),
        "recall": recall_score(y_test, y_pred),
        "f1": f1_score(y_test, y_pred)
    }
    mlflow.log_metrics(metrics)
    
    # Log model
    mlflow.sklearn.log_model(model, "random_forest_model")

Experiment Management Tools:

• MLflow
• Weights & Biases
• Neptune.ai
• Comet.ml
• DVC (Data Version Control)

Experiment Management Best Practices:

• Track all experiments, even failed ones
• Use consistent naming conventions
• Tag experiments for easy filtering
• Compare experiments systematically
• Link experiments to requirements
• Document findings and insights