ML Model Deployment – Batch vs Real-time Inference
30 Days of MLOps Challenge: Day 13
By Sandip Das · · 5 min read
💡
Hey — It's Sandip Das 👋
Understanding both batch and real-time inference is essential for MLOps engineers to choose the right deployment strategy based on latency, scalability, and use case requirements.
Mastery of these methods ensures efficient model serving, optimized resource usage, and better user experience in production environments.
Understanding both batch and real-time inference is essential for MLOps engineers to choose the right deployment strategy based on latency, scalability, and use case requirements.
Mastery of these methods ensures efficient model serving, optimized resource usage, and better user experience in production environments.
📚 Key Learnings
- Understand the difference between batch inference and real-time (online) inference
Read the full article on learnxops.com.
Topics: MLOps, ML Model Deployment, Community, 30 Days of MLOps