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Backend Engineer: Fresher to Infilect-Ready
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Module 8
Integrating and Serving Machine Learning Models
1
Training vs. Inference: The ML Lifecycle
Differentiate between Machine Learning model training and inference phases.
2
Loading Pre-trained Models in Python
Load a serialized, pre-trained Machine Learning model in a Python application.
3
Deploying ML Models with DRF
Wrap a pre-trained ML model's inference function inside a DRF API view.
4
Accelerating ML Inference with Celery Background Tasks
Offload ML model execution to a Celery background task to handle inference latency.
5
Robust Model Inferences: Timeouts and Fallbacks
Implement timeout configurations and graceful fallbacks for failed model inferences.
6
Mocking ML Inference for API Testing
Mock ML inference results to test backend API workflows without loading full models.
Previous module
Asynchronous Task Processing with Celery and Redis
Next module
Building Dataset Management Pipelines