MLOps (Machine Learning Operations) is an engineering function of a team composed of programmers, data scientists, and DevOps engineers tasked to train, deploy, and monitor machine learning (ML) models in production.
Implementing artificial intelligence (AI) and ML models requires continuous integration and deployment, and MLOps ensures tracking, validation, and governance.
In this definition...
Adopting machine learning in production presents a challenge as it consists of various components with different levels of complexity, from data ingestion and preparation to model training, deployment, and monitoring. Responding to the demands of the machine learning lifecycle requires collaboration. An ML lifecycle encompasses an array of stages, ranging from experimentation to continuous integration, delivery, and deployment.
Modeled after DevOps, MLOps orchestrates a team of ML engineers, data scientists, and IT experts and combines machine learning, app development, and IT operations into one environment.
With its sets of principles and best practices, MLOps is the key to the success of enterprise AI adoption. A typical ML model management system includes:
Data acquisition: A stage of data collection, ingestion, and preparation that integrates all acquired data for validation and analysis.
Development: Using labeled libraries of data to build and train ML models.
Pre-production: Involves the validation of the ML system and model evaluation to test its readiness for deployment.
Production: An iterative deployment and continuous monitoring of the ML model in production at scale.
MLOps focuses on machine learning projects, borrowing software engineering principles from DevOps, particularly the iterative approach to the writing, delivery, and deployment of enterprise applications.
The components of MLOps can be divided into three parts:
In adopting MLOps, an organization can realize these benefits: