In software development, DevOps ensures the SDLC lifecycle is followed using agile processes. Similar to DevOps, MLOps is used in machine learning projects. It focuses on streamlining the process of taking machine learning models to production and then maintaining and monitoring them.
MLOps stands for Machine Learning Operations.
Why MLOps is Required?
Below are a few reasons why MLOps is required:
- MLOps manages the complete lifecycle of the machine learning project. Machine learning pipelines are created, deployed into production, and then maintained and monitored over time.
- ML models can face problems like model drift. MLOps ensure figuring out such issues and retraining the model if required.
- The input and output are validated at each step in model pipelines like ensuring data quality, performing feature engineering steps, etc.
- MLOps also handles operations like testing, logging, and scaling of the model built.
Advantages of MLOps
Below are a few advantages of MLOps:
- Faster Model Deployment
- Reproducibility of Code
- Increase Productivity
- Maintainance and Monitoring of Model
- Overall Cost Reduction
Is MLOps Engineer a Thing?
MLOps Engineer is a new emerging career opportunity in the field of Data Science that is in high demand. You can go through this article to understand the skills required to become an MLOps Engineer.