ML Engineering

MLOps - From Notebook to Production, Done Right

Master the practices and tools for deploying, monitoring, and maintaining ML models in production.

CI/CD for ML - automate model training and deployment
Model monitoring - track performance and drift in production
Reproducibility - ensure consistent results across environments

Automation is not about replacing humans; it's about augmenting them.

MLOps — Machine Learning Operations

MLOps applies DevOps principles to ML — automating model training, deployment, monitoring, and maintenance.

MLOps Lifecycle

DfMLOps Lifecycle

\text{Data Pipeline} \rightarrow \text{Training} \rightarrow \text{Evaluation} \rightarrow \text{Deployment} \rightarrow \text{Monitoring}

With feedback loop from Monitoring back to Data Pipeline.

Tools:

Data: DVC, Feature Store, Great Expectations
Training: MLflow, Weights and Biases, DVC
Deployment: Docker, Kubernetes, Seldon
Monitoring: Evidently, Whylabs, Prometheus
Orchestration: Airflow, Kubeflow, Prefect

MLOps Lifecycle Diagram

Experiment Tracking

Example: MLflow Experiment Tracking

import mlflow

# Log experiment
mlflow.log_param("learning_rate", 0.001)
mlflow.log_param("batch_size", 32)
mlflow.log_metric("accuracy", 0.95)
mlflow.log_metric("loss", 0.12)

# Log model
mlflow.sklearn.log_model(model, "model")

CI/CD Pipeline for ML

Model Monitoring

DfModel Monitoring

What to monitor in production:

Data drift: Input distribution changes
Model drift: Performance degrades
Prediction drift: Output distribution changes
Latency/throughput: System performance

When drift detected -> trigger retraining.

Model Monitoring Dashboard

Key Takeaways

Summary: MLOps

MLOps = DevOps for ML
Experiment tracking is essential for reproducibility
Version control for data, code, and models
Automated pipelines reduce manual errors
Monitoring detects model degradation
A/B testing validates model updates
CI/CD for model deployment
Feature stores ensure consistent features

What to Learn Next

-> Model Deployment Deploy models to production environments.

-> Feature Stores Manage and serve features efficiently.

-> ML System Design Design scalable ML architectures.

-> Model Evaluation Measure model performance accurately.

-> Model Selection Choose the right model for your problem.

-> AutoML Automate machine learning workflows.

MLOps — Machine Learning Operations Complete Guide