By IT Brew Staff
less than 3 min read
Definition:
When executed correctly, MLOps allows organizations to effectively bring machine learning processes online and run them more effectively, which can help automate processes throughout the organization (such as data analytics). As the name implies, MLOps combines elements of DevOps, including continuous integration/continuous delivery (CI/CD) methodology, which automates the software development life cycle.
Bringing machine learning models online often involves specialists from a number of areas, including software engineering, data science, and IT infrastructure. By adopting a DevOps-style collaborative approach to deploying, using, and iterating on machine learning, MLOps aims to eliminate costly communication issues and weed out any wastefulness in workflows.
MLOps touches on the core steps of the machine learning model development process, from managing and gathering data, to developing and deploying the ML models, to monitoring and optimization. Organizations not employing MLOps for this cycle will need to deploy models manually, then find precious time to eliminate errors, refine the models, and scale effectively to meet teams’ needs; with MLOps’s pipeline automation and CI/CD, model training and deployment becomes more automated and easier for teams to handle; finally, adding version control, continuous monitoring, and other elements can further optimize processes like updating models with new data.
For organizations attempting to deeply integrate multiple machine learning models into their products and workflows, MLOps can quickly prove an essential way to save time and resources. The improved productivity can have significant real-world benefits.
