MLOps in 2025: Production Best Practices

The Discipline of Production ML

MLOps has matured significantly. 2025 sees standardized practices for deploying, monitoring, and maintaining ML systems in production.

Deployment Patterns

Batch Inference

• Run on schedule (daily/hourly)
• Process large volumes efficiently
• Use orchestrators like Airflow, Prefect

Real-time Inference

• REST API or gRPC endpoint
• Need low latency (p99 < 100ms)
• Scale with load balancers

Streaming Inference

• Process data streams (Kafka, Kinesis)
• Stateless or stateful (using Flink, Bytewax)
• Best for event-driven applications

Model Serving 2025

# Using BentoML
import bentoml
from transformers import pipeline

model = pipeline("text-generation", model="meta-llama/Llama-4-7b")

service = bentoml.Service("llama-service", runners=[model])

@service.api(input=bentoml.io.Text(), output=bentoml.io.Text())
def generate(prompt: str) -> str:
    return model(prompt, max_length=100)[0]["generated_text"]

# Serve: bentoml serve .

Popular Serving Platforms

• vLLM: High-throughput LLM serving
• TGI (Text Generation Inference): Hugging Face's optimized server
• Triton Inference Server: NVIDIA's enterprise serving
• BentoML: Python-native serving

Monitoring

Data Drift Detection

from evidently import ColumnMapping
from evidently.report import Report
from evidently.metrics import DataDriftTable

data_drift_report = Report(metrics=[DataDriftTable()])
data_drift_report.run(current_data=current, reference_data=reference, column_mapping=column_mapping)

Model Performance

• Concept drift: Relationship between features and target changed
• Prediction drift: Model output distribution changed
• Feature importance shift: Different features matter now

CI/CD for ML

# .github/workflows/ml-pipeline.yml
name: ML Pipeline
on: [push]

jobs:
  train-and-deploy:
    runs-on: gpu-runner
    steps:
      - uses: actions/checkout@v4
      - name: Train model
        run: python train.py
      - name: Run tests (accuracy, fairness, robustness)
        run: pytest tests/
      - name: Package model
        run: bentoml build
      - name: Deploy to staging
        run: bentoml deploy llama-service --env staging
      - name: Smoke tests
        run: python smoke_test.py
      - name: Promote to production
        run: bentoml deploy llama-service --env production

Feature Stores

Centralize feature engineering:

• Feast: Open source feature store
• Tecton: Enterprise feature platform
• Databricks Feature Store: Integrated with Lakehouse

ML Platforms 2025

• Kubeflow: Kubernetes-native ML
• Metaflow: Human-centric data science
• ZenML: MLOps framework for all stacks