“MLOps, or Machine Learning Operations, is the practice of applying DevOps principles and practices to the lifecycle management of machine learning models.”
The components of MLOps include:
→ Exploratory data analysis (EDA)
→ Data Prep and Feature Engineering
→ Model training and tuning
→ Model review and governance
→ Model inference and serving
→ Model Monitoring
→ Automated model retraining
MLOps addresses challenges related to model drift and data quality. It allows for models to be monitored in real-time, detect any performance degradation, and trigger re-evaluation or retraining if necessary, ensuring that models are as accurate as possible.
What are some benefits of MLOps?
- Efficiency – allows data teams to achieve faster model development
- Scalability – enables the ability for thousands of models to be controlled and managed for continuous deployment
- Risk reduction – greater transparency and faster response to regulatory requests
By applying DevOps principles and practices, organizations can optimize their machine learning workflows, accelerate time to market, and improve model performance.