MLOps, short for Machine Learning Operations, is a practice for collaboration and communication between data scientists and operations professionals to help manage machine learning (ML) and AI in production. It is an interdisciplinary field that bridges the gap between machine learning or data science and software development with an aim to streamline the deployment, testing, and maintenance of ML models in a continuous manner.
How MLOps works
MLOps works through a combination of practices from both machine learning and DevOps. For a machine learning model to be sustainable, it has to be monitored, updated, and governed, and MLOps facilitates all these tasks.
In the MLOps lifecycle, data scientists first develop a model and test its algorithms by using agile principles for faster experimentation. Once the model is fine-tuned and ready, developers implement it into the application and operations professionals manage the infrastructure needed to run it.
These models are continuously monitored for their performances and any change in the accuracy of a model is identified instantly. In the event of a performance drop, the model can be retrained with new data and updated.
MLOps also emphasizes on automation and the monitoring of all steps of ML system construction, including integration, testing, releasing, deployment, and infrastructure management. It aims to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements.
By following the MLOps principles, your team will be able to deliver more reliable models, increase efficiency, and reduce risks associated with machine learning projects.
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