What is the main difference between MLOps and DevOps?

DevOps focuses on software delivery, infrastructure, automation, and system reliability. MLOps extends DevOps practices to manage the machine learning lifecycle, including data pipelines, model training, experiment tracking, deployment, monitoring, drift detection, and retraining.

Can MLOps and DevOps work together?

Yes. DevOps manages infrastructure, deployment pipelines, and application reliability, while MLOps manages data workflows, model lifecycle, model monitoring, and retraining. Most modern AI-powered products need both working together.

Why is MLOps needed if DevOps already exists?

DevOps is not designed to manage model drift, data versioning, experiment tracking, retraining pipelines, or statistical model validation. MLOps is needed because machine learning systems depend on changing data and model performance, not just stable code and infrastructure.

What tools are used in MLOps?

Common MLOps tools include MLflow and Weights & Biases for experiment tracking, DVC for data versioning, Kubeflow and Airflow for orchestration, SageMaker and Seldon for deployment, and WhyLabs or Prometheus for monitoring.

How can businesses start with MLOps?

Businesses can start with MLOps by first building a strong DevOps foundation, then adding data pipelines, experiment tracking, model versioning, automated retraining, model monitoring, and drift detection as machine learning becomes more important to the product.

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Bhargav Bhanderi

Bhargav Bhanderi is a Director at Creole Studios, where he leads strategic initiatives across software development, cloud, and AI-driven solutions. With a strong focus on execution and business outcomes, he works closely with global clients to deliver scalable, high-impact digital products and engineering solutions.