What is MLOps?
According to Gartner, “The process and framework to operationalize the ML Pipeline is called machine learning operationalization (MLOps). MLOps aims to standardize the deployment and management of ML models alongside the operationalization of the ML pipeline. “It helps support the release, activation, monitoring, performance tracking, reuse, maintenance, and governance of ML artifacts. The MLOps process supports the CI/CD framework, and it derives its core principles from the best practices of DevOps. Four reasons why MLOps is needed are as follows:
- Issues with Deployment
- Issues with Monitoring
- Issues with Lifecycle Management
- Issues with Model Governance
Why Does MLOps matter?
MLOps is fundamental. Machine learning helps individuals and businesses deploy solutions that unlock previously untapped sources of revenue, save time, and reduce cost by creating workflows, building models, leveraging data analytics for decision-making, and improving customer experience.
This is where MLOPs comes into picture. It assists with establishing a framework to develop, manage, deploy, monitor, and scale the delivery of ML models. It brings together capabilities, personas, and functions across the development and operationalization stages. It drives collaboration among different roles and responsibilities across the development and operationalization stages of the machine learning development life cycle.
Automating model development and deployment with MLOps means faster go-to-market times and lower operational costs. It helps managers and developers be more agile and strategic in their decisions. It helps ensure model governance by tracing audit trails, model results, and upgrades approval workflows.