// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
MLOps
MLOps refers to the practices for deploying and maintaining machine learning models in production reliably and efficiently.
TECHNICAL DEFINITION
MLOps integrates DevOps principles with machine learning workflows, encompassing model development, deployment, monitoring, and governance to ensure scalable and reliable ML systems.
BACKGROUND
AI observability is the practice of collecting and analyzing telemetry, data such as logs, metrics, and traces that a system automatically records as it runs, from artificial intelligence systems deployed in production, in order to understand how they behave and how well they perform in terms of accuracy, cost, and safety. It applies the idea of observability, which comes from control theory and software engineering, to systems based on machine learning models, and in particular to large language models (LLMs) and autonomous agents. In control theory, observability is a measure of how well the internal state of a system can be inferred from its external outputs.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Machine Learning Operations
- ML Ops
- AI Ops
USAGE NOTE
MLOps aims to bridge the gap between data scientists and operations teams.
DEVELOPERS
Organizations developing technology related to MLOps.
Google Cloud's Vertex AI is a unified platform for building, deploying, and scaling machine learning models, offering comprehensive MLOps capabilities including experiment tracking, model management, deployment, and monitoring.
Azure Machine Learning provides an enterprise-grade MLOps platform to build, train, and deploy machine learning models faster. It includes features for MLOps such as CI/CD, data drift detection, and model monitoring.
Amazon SageMaker offers a comprehensive set of MLOps tools and integrations to simplify the process of building, training, and deploying ML models at scale, including capabilities for continuous integration and delivery.
Databricks provides a unified platform for data and AI, incorporating MLOps capabilities through features like MLflow for tracking experiments, packaging code, and deploying models, all built on their Lakehouse architecture.
Weights & Biases offers a developer toolkit for machine learning that helps teams track experiments, visualize model performance, manage datasets, and streamline MLOps workflows from research to production.
Seldon specializes in MLOps for deploying, managing, and monitoring machine learning models in production, offering an open-source framework and an enterprise platform for scalable and reliable ML operations.
Comet ML is an MLOps platform that provides tools for experiment tracking, model production, and model monitoring across the entire machine learning lifecycle, helping teams collaborate and accelerate their ML projects.
Verta.ai offers an enterprise MLOps platform designed to help organizations develop, deploy, and operate machine learning models efficiently and responsibly at scale, providing model versioning, monitoring, and governance.