What are the Benefits of MLOps?
Machine Learning Operations’s target is to bridge the gap between collaborating teams which can save time and help everyone align with the business objectives. Let’s discuss the benefits of MLOps that bridge this gap:
- MLOps provides a team collaborative environment and cross-functional communication that facilitates a smooth transition from development to deployment.
- Practices keeping track of changes done in the model allows rolling back to previous versions if necessary and applies to the ML models which is similarly done in software development.
- Continuous monitoring helps in detecting issues like a decrease in performance and provides solutions and responses to the issue automatically with less human interference.
- Machine Learning models, their training, and deployment can consume huge amounts of computational resources whereas MLOps optimizes these computational resources and makes them cost-effective, by automating the tasks, and scaling infrastructure.
- In the machine learning life cycle, to eradicate or reduce the number of errors and inconsistencies, some standardized processes are introduced by MLOps for development, testing, deployment, and monitoring, by adhering to some regulatory requirements and governance standards.
MLOps: Everything You Need to Know
The demand for data-related roles has been on a constant rise in recent years. The percentage of people shifting to the data industry from different technical backgrounds is growing constantly. Data Science and Analytics jobs are the highly competitive job roles that most people dream of.
However, other areas in the data industry are less known. Such is Machine Learning Operations (MLOps). Read the article till the end to learn about What is MLOps and to whom it is for.