๐ท MLOps Explained โ Model Deployment Patterns: Batch, Real-Time & Edge
๐ Why Model Deployment Is Not One-Size-Fits-All
Deploying a machine learning model is not just about making predictions available.
Deployment decisions affect:
System architecture User experience Operational cost Model performance and reliability
Different use cases demand different deployment patterns. MLOps provides the tools and discipline to support all of them.
๐งฉ What Is Model Deployment in MLOps?
In MLOps, model deployment means:
Packaging a trained model Exposing it for inference Integrating it with production systems Monitoring its behaviour over time
Deployment is not a one-time event โ it is a managed lifecycle.
๐ฆ Batch Deployment
๐น What Is Batch Inference?
Batch deployment runs predictions on large volumes of data at scheduled intervals.
Typical characteristics:
Offline processing High throughput Low infrastructure cost No strict latency requirements
๐น Common Use Cases
Customer segmentation Churn prediction Fraud analysis Recommendation generation Reporting and analytics
Batch inference is ideal when real-time responses are not required.
๐น MLOps Considerations
Scheduling and orchestration Data freshness guarantees Model version consistency Output storage and lineage
Batch pipelines must be reliable and reproducible.
โก Real-Time Deployment
๐น What Is Real-Time Inference?
Real-time deployment serves predictions instantly via APIs.
Typical characteristics:
Low-latency responses Always-on services Scalable infrastructure
๐น Common Use Cases
Search ranking Fraud detection Personalisation Dynamic pricing
Real-time inference is critical when decisions must be immediate.
๐น MLOps Considerations
API reliability and scaling Model rollback strategies Latency monitoring Traffic shaping and canary releases
MLOps ensures real-time systems remain stable under load.
๐ Edge Deployment
๐น What Is Edge Inference?
Edge deployment runs models directly on devices โ not in the cloud.
Typical characteristics:
Local execution Low latency Reduced network dependency Privacy benefits
๐น Common Use Cases
IoT devices Autonomous systems Mobile applications Industrial sensors
Edge inference is essential when connectivity or latency is constrained.
๐น MLOps Considerations
Model size optimisation Hardware constraints Update and rollout strategies Security and version control
Edge deployments require careful operational planning.
๐ Hybrid Deployment Patterns
Many real-world systems use multiple deployment patterns together.
Examples:
Batch training + real-time inference Cloud inference + edge fallback Offline scoring + online re-ranking
MLOps enables consistency across hybrid environments.
โ ๏ธ Deployment Challenges Without MLOps
Without MLOps, teams face:
Manual deployments Inconsistent model versions Undetected failures Slow rollbacks Production incidents
Deployment becomes a risk instead of a controlled process.
๐ง Why Deployment Patterns Matter
Choosing the right deployment strategy enables organisations to:
Meet performance requirements Control costs Scale safely Maintain model quality
MLOps turns deployment from an afterthought into a strategic decision.
๐ Where This Episode Fits
This episode explains:
How ML models are deployed in production Why different patterns exist What operational trade-offs matter
It prepares you for the next challenge: monitoring models once they are live.
๐ฎ Whatโs Next?
๐ Once models are deployed โ how do we know they are still performing well?
The next episode explores Monitoring Models in Production, covering drift detection, performance tracking, and alerting.
