DEPLOYMENT /// MLOPS /// FASTAPI /// DOCKER /// MONITORING /// DATA DRIFT /// PRODUCTION ///

Deploying Forecasts

Bridge the gap between Research and Production. Serve your models via REST APIs and monitor for model decay.

System Log

"A forecasting model is useless if it stays in a notebook. Deployment turns predictions into actionable business decisions."

Concept Integration

Which of the following statements best describes the concept: API Architect?

A. It is completely unrelated to the core mechanics discussed.B. Built a FastAPI wrapper for inference.C. It bypasses the need for training entirely.

Production Workflows

REST Inference

Serving forecasts requires handling Shape Mismatch. Your API must ensure the input lag features match the training window (e.g., last 30 days of data).

Drift Detection

Time series data is non-stationary. If the mean shifts (e.g., due to inflation), your model's accuracy will crash. Implement KL-Divergence monitoring.

# Dockerizing for Scalability

FROM python:3.9-slim

COPY requirements.txt .

RUN pip install -r requirements.txt

CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "80"]

Challenge 1

Validate the input JSON schema for a weather forecast model.

Challenge 2

Identify 'Data Drift' in a live chart of retail sales.

A.I.D.E. Consult

Ask the AI Assistant about Latency vs Accuracy trade-offs.

Glossary of Deployment Terms

Model Decay: The degradation of predictive power as real-world data evolves away from training data.
Inference Latency: The time taken for a model to generate a forecast after receiving a request.
Feature Store: A centralized repository for pre-calculated time series features used during inference.
Stationarity Shift: When the statistical properties of the series change in production, necessitating a retrain.