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 sitting on a hard drive. MLOps ensures:

• Reproducibility: Can you recreate the model a month from now?
• Versioning: Which data was used to train this specific version?
• Monitoring: Is the model's accuracy "drifting" in production?

The MLOps Stack

• DVC: Version control for data.
• MLflow: Tracking experiments and models.
• Kubeflow: Running ML workflows on Kubernetes.

Future Proofing

At Solardevs, we help companies build the infrastructure that allows their AI models to grow and adapt without constant manual intervention.