31. Model Deployment#
Once a model is trained, it can then be deployed. Deployment strategies vary based on use cases and needs. Some examples:
In Applications – Models can be integrated into mobile, desktop, or web apps for real-time predictions.
On Edge (on device) Deployment – Running models on local devices without needing internet connectivity.
API Deployment – Hosted on a server, allowing external applications and users to send requests and receive predictions via an API.
Via Batch Processing Pipelines – Running large datasets through the model at scheduled intervals.
In Active Learning & Model-Assisted Labeling Systems – Humans review model-generated predictions, verifying or correcting them to quickly build more ground truth data. This feedback loop helps retrain and improve the model over time.
In a Semi-Supervised Learning Pipeline A mix of labeled and unlabeled data is used to improve performance, reducing the need for fully annotated datasets.
Self-Supervised Learning Pipeline The model learns patterns from data without human-labeled annotations, useful for feature extraction and pretraining.
Each deployment approach has trade-offs in terms of latency, cost, scalability, and human oversight.