Machine learning operations (MLOps) is a field in machine learning (ML) that is focused on automating the deployment, monitoring and managing of ML models that are moved into production, allowing it to be scalable so that business value can be created. Different teams within an organization (Data Scientists, DevOps engineers etc.), processes, practices and technologies are integrated to build MLOps pipelines.
An MLOps pipeline is responsible for managing the training, creation, storage, deployment and monitoring of ML models that are moved into production. The MLOps pipeline follows continuous integration and continuous delivery (CI/CD) principles ensuring that the model is tested sufficiently, can integrate into the pipeline well, and changes to the pipeline are implemented automatically.
There are various reasons as to why an organization using ML and AI should use an MLOps pipeline. One of the main reasons is so that an organization can reap the benefits of using ML through understanding how to scale their models and deploy them into production. MLOps allows for organizations to take advantage of their ML models by providing them with a solid foundation to automate and scale the ML lifecycle. MLOps allows for different teams that are responsible for developing and deploying ML models to collaborate effectively on a stable platform. If it becomes easier for organizations to take advantage of their ML models, they can more easily achieve their AI-driven strategic goals.
There are four key areas in MLOps that benefit organizations greatly. The first key area is model deployment. ML models are built using different programming languages and platforms, which can cause instabilities when deploying into production. A benefit of MLOps is that it streamlines the deployment process allowing for continuous access to the necessary APIs regardless of the deployment environment or cloud services used. ML models are in essence “plugged” into the pipeline. The second key area is model monitoring. Over time, ML models can degrade due to training data not being representative of the current trends. This results in incorrect and unreliable predictions. MLOps allows you to continuously monitor ML models in terms of data drift, accuracy and feature importance, and is able to inform the user when predictions are bad or not complying with a set standard of quality. The third key area is model life cycle management which becomes more critical as the number of ML models used in organizations in production starts increasing. It is important that the different phases in the ML lifecycle of a model are as automated as possible, including, updates, approvals, troubleshooting etc. The last key area is production model deployment. Organizations are required to ensure that their ML models deployed into production comply with respective regulations and that updates, deployments and approvals are tracked appropriately. MLOps automates documentation, audit trails and allows for model version control which ensures that the governance risks in organizations are minimal.
MLOps can help organizations maintain a competitive advantage since the number of businesses that are applying ML into their workflows is increasing drastically. MLOps ensures that ML models produce accurate predictions quickly using the most current, available data. In order for organizations to achieve better business results, minimize costs and reap the benefits of their AI projects, organizations need to have MLOps in place to enhance the scalability of their ML and AI initiatives. Contact us today to get you started! email@example.com