10 MLOps Best Practices + How MLOpsCrew Implements Them

MLOps Best Practices: 10 Clear Steps to Build Reliable ML Systems

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Bimal S

Machine learning projects rarely fail because of the algorithms themselves. They fail because of messy data pipelines, ad-hoc deployments, and no way to reproduce results. MLOps (Machine Learning Operations) is the discipline that fixes this—combining DevOps principles with data science to make ML reliable, auditable, and scalable.

Yet most “best practice” lists are vague. This article walks you through 10 concrete steps, each with code or a visual example, so you can build a truly production-grade ML workflow.

1. Track Everything – Code, Data and Models

ML models are only as reproducible as the artefacts you keep. Version control is non-negotiable for all three pillars:

Code (training scripts, feature engineering code)
Data (raw and processed datasets)
Models (artefacts, metadata, metrics)

A simple way to get started is Git + DVC (Data Version Control):

bash

git init

dvc init

dvc add data/raw/customers.csv

git add . && git commit -m "Track data and code"

This ties each dataset version to a Git commit. Pair it with a model registry (MLflow, SageMaker, or Vertex AI) so every model artefact is linked back to the exact code and data used to produce it.

2. Automate Your ML Workflow with CI/CD

Manual deployments are error-prone and slow. In MLOps, continuous integration (CI) means automatically testing and training models on every code/data change, and continuous delivery (CD) means pushing validated models into staging or production automatically.

Here’s a mini GitHub Actions pipeline:

yaml

name: mlops-ci

on: [push]

jobs:

train:

runs-on: ubuntu-latest

steps:

- uses: actions/checkout@v3

- name: Install deps

run: pip install -r requirements.txt

- name: Validate data

run: python scripts/data_validation.py

- name: Train model

run: python train.py

- name: Register model

run: mlflow models register -m ./model

This pipeline installs dependencies, validates data, trains a model, and registers the artefact.

3. Keep a Log of Experiments and Use a Model Registry

When you try dozens of hyperparameter combinations, it’s easy to lose track of what worked. Experiment tracking tools (MLflow, Weights & Biases, Neptune) log parameters, metrics and artefacts automatically. A model registry then becomes your “source of truth” for production-ready models.

Example using MLflow:

python

import mlflow

mlflow.start_run()

mlflow.log_param("n_estimators", 200)

mlflow.log_metric("AUC", auc_score)

mlflow.sklearn.log_model(model, "model")

mlflow.end_run()

Now every run is logged and you can compare metrics, view plots, and promote a model to production with a single click.

4. Validate Data Before It Breaks Your Models

Bad data is the silent killer of ML systems. Schema changes, missing values or outliers can degrade a model overnight. The fix is automated data validation in your pipeline.

Great Expectations makes it easy:

python

from great_expectations.dataset import PandasDataset

dataset = PandasDataset(df)

dataset.expect_column_values_to_not_be_null("customer_id")

dataset.expect_column_values_to_be_between("age", 18, 99)

dataset.save_expectation_suite("customer_data_suite.json")

ntegrate this script into your CI/CD job. If the data fails the expectations, the pipeline halts before training or deployment.

5. Test Your Models Like You Test Your Code

In software, unit and integration tests catch regressions. ML needs similar checks:

Unit tests for data preprocessing functions
Integration tests for full pipelines
Regression tests to ensure model quality hasn’t dropped
Fairness tests to catch bias

A minimal regression test:

python

def test_model_auc():

y_pred = model.predict(X_test)

assert roc_auc_score(y_test, y_pred) > 0.80

Run this automatically in CI. For fairness audits, tools like AIF360 or Fairlearn can flag disparities between groups.

6. Make Everything Reproducible with Containers and Infra-as-Code

Even if code and data are versioned, your environment may differ. Use containers (Docker) plus infrastructure-as-code (Terraform/CloudFormation) to guarantee identical setups across dev, staging and production.

Basic Dockerfile:

dockerfile

FROM python:3.11-slim

WORKDIR /app

COPY requirements.txt .

RUN pip install -r requirements.txt

COPY . .

CMD ["python", "predict.py"]

Terraform ensures the same GPU/CPU infrastructure is provisioned automatically.

7. Watch Your Models in Production for Drift and Errors

Deployment isn’t the finish line. Data distributions change, features get new ranges, and performance drifts. You need real-time monitoring for:

Model accuracy vs. ground truth (when available)
Input feature distributions
Latency, throughput, cost

Minimal Evidently drift-report:

python

from evidently.report import Report

from evidently.metrics import DataDriftPreset

report = Report(metrics=[DataDriftPreset()])

report.run(reference_data=ref_df, current_data=prod_df)

report.save_html("drift_report.html")

Run this daily or weekly and alert your team if drift exceeds a threshold.

Feature duplication wastes time and introduces inconsistencies. A feature store centralizes feature definitions, ensuring training and serving use the same values.

Quick Feast example:

python

from feast import FeatureStore

store = FeatureStore(repo_path="feature_repo")

features = store.get_historical_features(

entity_df=entity_df,

feature_refs=["customer:age", "customer:avg_txn_amount"]

).to_df()

This retrieves consistent features for model training or inference.

9. Build in Security, Governance and Compliance

As ML moves into regulated industries, auditability and explainability matter as much as performance. At minimum, implement:

Role-based access control (RBAC)
Audit logs for data and model changes
Encryption at rest and in transit
Model cards documenting intended use and limitations

Simple model card template:

markdown

# Model Card: Customer Churn Predictor

Version: 1.2

Owner: Data Science Team

Intended Use: Predict customer churn

Ethical Considerations: Biased data warning

Performance Metrics: AUC = 0.87 (last validated 2025-09-10)

10. Plan for Retraining and Keep an Eye on Costs

Models degrade. Plan when and how to retrain (time-based or event-based triggers). Also monitor resource usage—ML infra can get expensive fast.

Simple Airflow DAG orchestrating retraining:

python

from airflow import DAG

from airflow.operators.python import PythonOperator

from datetime import datetime

with DAG("retrain_model", start_date=datetime(2025, 9, 1),

schedule_interval="@weekly") as dag:

retrain = PythonOperator(task_id="train",

python_callable=retrain_model)

Pair this with autoscaling, spot GPUs, or serverless endpoints to cut costs.

Bonus: MLOps Maturity Checklist

Stage	Experiment Tracking	Deployment	Monitoring
Starter	Manual in notebooks	Manual copy of model file	None
Intermediate	MLflow logging	CI/CD to staging	Basic drift check
Advanced	MLflow + registry + feature store	Canary/blue-green deploy	Real-time metrics & auto retraining

How MLOpsCrew Helps You Implement These Best Practices

For many teams, the challenge isn’t knowing what to do but executing these practices consistently across fast-moving projects, multiple stakeholders, and strict compliance requirements.

That’s where MLOpsCrew comes in. We’re a specialised team focused solely on designing, building, and operating production-grade MLOps pipelines for organisations of all sizes.

End-to-End Setup: Implement Git + DVC, MLflow, and registries for full version control.
Automated CI/CD: Build pipelines to validate, train, and deploy models with zero manual effort.
Data Quality & Monitoring: Integrate Great Expectations + Evidently to catch issues early.
Reproducible Infra: Use Docker, Kubernetes, Terraform, and feature stores for scalability.
Governance & Compliance: Enable RBAC, audit logs, and model cards for secure ML ops.
Retraining & Lifecycle: Automate retraining with Airflow/DAGs and set up monitoring dashboards.

Book 45-minute free consultation with our MLOps Experts to discuss how you can utilize these best practices in your organization.