Definition of MLOps
MLOps stands for Machine Learning and Operations. It’s like DevOps, but for machine learning (ML). It’s all about making the process of building, deploying, and maintaining ML models more efficient. MLOps focuses on ensuring ML projects run smoothly from start to finish.
Examples of MLOps
- Versioning Data and Models. A team is working on a recommendation engine for a website. They keep versions of datasets and models. It’s to understand what changes led to improvements or drops in performance. They can roll back to a previous state if needed using a tool like DVC.
- Continuous Integration/Continuous Deployment (CI/CD). An e-commerce company has a pricing model is updated daily with new data. When fresh data comes, the model retrains. If it passes performance checks, it is deployed to production.
- Model Monitoring. A credit scoring model in a bank monitors predictions in real time. If the model’s accuracy drops or if there’s a sudden change in the data (data drift), the model alerts the team.
- Scalability. A startup provides image recognition as a service. It uses Kubernetes to deploy its ML models. When their user base grows, Kubernetes scales the number of model servers to handle the increased load.
- Experiment Tracking. Researchers at a pharmaceutical company are working on predicting molecular properties. They conduct experiments with different models and hyperparameters. Using a tool like MLflow, they keep track of all their experiments and can compare results.
- Automated Testing. A team is deploying a chatbot model to a customer support page. They use automated testing to simulate user questions and check the chatbot’s responses. If it doesn’t meet the accuracy threshold, it’s not deployed.
- Collaboration. A corporation has multiple teams across the world working on various ML projects. They use platforms like Kubeflow, which offers a shared workspace. Teams can collaborate on building, training, and deploying models.
- Security and Governance. A health-tech company working with patient data. The company ensures that its ML pipelines are compliant with health data regulations. They have automated checks to ensure no personally identifiable information is exposed and that model decisions can be audited.