What is the difference between ML and MLOps?

Amelia

Machine Learning (ML) focuses on building models that learn from data to make predictions or decisions, covering tasks like data preparation, training, and evaluation. MLOps, on the other hand, manages the lifecycle of those ML models in production, including deployment, monitoring, versioning, and continuous improvement. In simple terms, ML builds the model, while MLOps ensures it runs reliably and scales in real-world environments. How does your team balance model development with production management in your ML projects?

Jessica

Our team balances model development and production management by clearly separating the roles of data science and operations while fostering close collaboration. During the model development phase, the focus is on experimenting with different algorithms, data preparation techniques, and model architectures. Once a model is trained and evaluated, it goes through versioning and validation to ensure it’s production-ready. For production management, we use MLOps practices to automate deployment, monitor model performance, and ensure scalability. Tools like Kubeflow and Docker help us package and deploy models consistently across environments. Continuous monitoring is implemented to track model drift and performance, and we’ve set up automated retraining pipelines to ensure the model stays accurate as new data arrives. This approach allows us to continuously improve the model while ensuring it runs reliably in production, enabling us to handle both development and operational challenges efficiently.