MLOps Pipelines

Model Deployment Strategies

Batch Deployment

• Description: Run model on fixed schedule on accumulated data
• Use Cases: Credit scoring, churn prediction, recommendations
• Advantages: Simple, cost-effective, handles large volumes
• Challenges: Latency, stale predictions
• Tools: Apache Airflow, dbt, cron jobs, cloud batch services

Real-time Deployment

• Description: Serve model as API for immediate predictions
• Use Cases: Fraud detection, dynamic pricing, personalization
• Advantages: Low latency, fresh predictions
• Challenges: Scalability, infrastructure complexity
• Tools: Flask, FastAPI, TensorFlow Serving, TorchServe, KServe

Edge Deployment