Implementing MLOps: CI/CT/CD For Robust Machine Learning Systems

MLOps, the application of DevOps principles to machine learning (ML), is essential for building scalable and reliable ML systems. The process involves continuous integration (CI), continuous testing (CT), and continuous delivery (CD) to streamline ML workflows.

MLOps Levels Explained

1. MLOps Level 0 (Manual Process)

Data scientists manually hand over trained models to engineering teams.
No tracking or logging of model predictions.

2. MLOps Level 1 (Automated Training & Deployment)

Introduces automation in ML pipelines.
Includes automated data validation, model validation, and metadata management.

3. MLOps Level 2 (Full CI/CD Automation)

Source control, testing, and deployment services are integrated.
Uses a model registry, feature store, and ML metadata store.
Pipeline orchestration tools like Kubeflow or Airflow are employed.

Pipeline Stages in MLOps

Development & Experimentation → Model prototyping.
Continuous Integration (CI) → Code and model versioning (Git).
Continuous Delivery (CD) → Automated deployment (Docker, Kubernetes).
Automated Triggering → Event-driven model retraining.
Monitoring → Logging (Prometheus, Grafana) and drift detection.

You Should Know: Essential Commands & Tools

1. Version Control (Git)

git clone <repo-url> 
git checkout -b feature-branch 
git commit -m "Update model training script" 
git push origin feature-branch

2. Containerization (Docker)

docker build -t ml-model:v1 . 
docker run -p 5000:5000 ml-model:v1

3. Orchestration (Kubernetes)

kubectl apply -f deployment.yaml 
kubectl get pods 
kubectl logs <pod-name>

4. ML Metadata Tracking (MLflow)

import mlflow 
mlflow.set_experiment("MLOps-Demo") 
mlflow.log_param("epochs", 50) 
mlflow.log_metric("accuracy", 0.92)

5. Automated Testing (Pytest)

pytest test_model.py

6. Monitoring (Prometheus + Grafana)

prometheus --config.file=prometheus.yml 
grafana-server --config=/etc/grafana/grafana.ini

What Undercode Say

MLOps bridges the gap between ML development and production, ensuring reproducibility, scalability, and reliability. Key takeaways:
– Version everything (code, data, models).
– Automate testing to catch errors early.
– Monitor models in production for performance decay.
– Use CI/CD pipelines for seamless deployments.

Expected Output: A fully automated ML pipeline with versioned models, automated testing, and real-time monitoring.

Reference: Google Cloud MLOps Guide