What Is MLOps? The Bridge Between Data Science and DevOps

The AI Lifecycle Problem

Data scientists are great at building models in notebooks. Engineers are great at building APIs. MLOps is the discipline that connects them.

Why MLOps Matters

Without a solid MLOps pipeline, model weights are just files on a disk. MLOps ensures:

• Reproducibility: Can you reproduce the model in a month?
• Versioning: What data was used to train this version?
• Monitoring: Is model accuracy "drifting" in production?

The MLOps Stack

• DVC: Version control for data
• MLflow: Experiment and model tracking
• Kubeflow: Running ML pipelines on Kubernetes

Preparing for the Future

At SolarDevs we help companies build the infrastructure that lets their AI models grow and adapt without constant manual intervention.