MLOps Pipeline in Practice: Automating from Model Training to Deployment

Sat, 25 Jul 2026 00:00:00 +0000

1. MLOps Pipeline Overview

MLOps brings DevOps principles to the machine learning lifecycle, automating model training, evaluation, deployment, and monitoring. A well-designed MLOps pipeline can significantly shorten the time from experimentation to production deployment.

2. Training Pipeline Implementation

Below is a training pipeline implementation in Python, covering the complete workflow of data loading, model training, and evaluation.

Python Training Pipeline

 
 
 
 

 python
 
 
import mlflow
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import accuracy_score, precision_score

def train_pipeline(data_path: str) -> float:
 """End-to-end training pipeline"""
 mlflow.set_experiment("production-model")

 with mlflow.start_run():
 # Data loading
 df = pd.read_parquet(data_path)
 X = df.drop("target", axis=1)
 y = df["target"]

 # Train-test split
 X_train, X_test, y_train, y_test = train_test_split(
 X, y, test_size=0.2, random_state=42
 )

 # Model training
 model = GradientBoostingClassifier(
 n_estimators=300,
 max_depth=6,
 learning_rate=0.1,
 )
 model.fit(X_train, y_train)

 # Model evaluation
 y_pred = model.predict(X_test)
 acc = accuracy_score(y_test, y_pred)
 prec = precision_score(y_test, y_pred, average="weighted")

 mlflow.log_metrics({"accuracy": acc, "precision": prec})
 mlflow.sklearn.log_model(model, "model")

 return acc

if __name__ == "__main__":
 accuracy = train_pipeline("s3://data/features/train.parquet")
 print(f"Model accuracy: {accuracy:.4f}")

Kubernetes Deployment Configuration

 
 
 
 

 yaml
 
 
apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
 name: production-model
spec:
 predictor:
 sklearn:
 storageUri: s3://mlflow-artifacts/1/model
 resources:
 requests:
 cpu: "1"
 memory: 2Gi
 limits:
 cpu: "2"
 memory: 4Gi

3. Summary

The core of an MLOps pipeline lies in automation and reproducibility. By standardizing the training, evaluation, and deployment processes, teams can iterate on models more rapidly while maintaining production stability.

Machine Learning on SRE & AI Field Notes

MLOps Pipeline in Practice: Automating from Model Training to Deployment

1. MLOps Pipeline Overview

2. Training Pipeline Implementation

Python Training Pipeline

Kubernetes Deployment Configuration

3. Summary