MLOps, or Machine Learning Operations, is a set of practices and tools used to streamline and manage the lifecycle of machine learning models. MLOps aims to ensure that models are developed, deployed, and maintained efficiently, reliably, and at scale.
Advantages of MLOps:
- MLOps is a critical practice for organizations that rely on machine learning models. One of the key advantages of MLOps is continuous training of models. With MLOps, we can set up a process for continuous training, which is important as data changes over time, affecting the model output. Continuous training ensures that models are up-to-date with the latest data, and the model output remains consistent.
- Another critical aspect of MLOps is experiment tracking. When we develop a machine learning model, we run many experiments to determine the optimal configuration. Experiment tracking helps to record the experiments automatically and saves the best configuration. This saves time and resources, as we don’t need to repeat the same experiments to find the optimal configuration.
- Data drift is another critical issue that MLOps helps to address. Data drift occurs when the data changes over time, and the model performance deteriorates. MLOps provides tools to detect data drift and take corrective action to maintain model performance.
- A model registry is another important aspect of MLOps. It is a central repository that stores all the information related to the model, including data, configurations, environment variables, model code, versions, and documentation. A model registry ensures that all key values are in one place, and everyone responsible has access. It helps to version the models and ensures faster deployments.
- Visualization is another critical aspect of MLOps. Visualization of different machine learning metrics, performance scores, and experiments helps to identify patterns and gain insights into the model performance. There are many tools available to speed up machine learning development by providing pre-built visualization.
- Finally, monitoring is a critical aspect of MLOps. Monitoring helps to collect statistics on the model performance based on live data. The output of this stage is a trigger to execute the pipeline or to execute a new experiment cycle. Monitoring also helps to track usage statistics, performance monitoring, application, and system-level logging, among other things. There are various tools…