Data Science — MLOps

What is MLOps?

Managing ML lifecycle from development to production.

MLOps components

1. Version control: Code and data
2. Experiment tracking: Models and metrics
3. Model registry: Model versions
4. Deployment: Serving models
5. Monitoring: Performance tracking

MLflow

import mlflow

# Log experiment
mlflow.log_param("learning_rate", 0.01)
mlflow.log_metric("accuracy", 0.95)
mlflow.log_artifact("model.pkl")

DVC (Data Version Control)

# Initialize
dvc init

# Track data
dvc add data.csv

# Push to remote
dvc push

Model serving

from fastapi import FastAPI
import pickle

app = FastAPI()
model = pickle.load(open("model.pkl", "rb"))

@app.post("/predict")
def predict(data: dict):
    return {"prediction": model.predict([data["features"]])}

Best practices

1. Automate pipelines
2. Version everything
3. Monitor models
4. Document experiments

Mini Practice

1. Track experiments with MLflow
2. Version data with DVC
3. Deploy a model
4. Set up monitoring

Up Next

Continue with Model Deployment - Production ML.