- ML Ops: Machine Learning Operations
Machine Learning Operations Machine Learning Operations With Machine Learning Model Operationalization Management (MLOps), we want to provide an end-to-end machine learning development process to design, build and manage reproducible, testable, and evolvable ML-powered software
- MLOps Principles
MLOps Principles As machine learning and AI propagate in software products and services, we need to establish best practices and tools to test, deploy, manage, and monitor ML models in real-world production In short, with MLOps we strive to avoid “technical debt” in machine learning applications SIG MLOps defines “an optimal MLOps experience [as] one where Machine Learning assets are
- State of MLOps
MLOps must be language-, framework-, platform-, and infrastructure-agnostic practice MLOps should follow a “convention over configuration” implementation The MLOps technology stack should include tooling for the following tasks: data engineering, version control of data, ML models and code, coninuous integration and continuous delivery
- MLOps Stack Canvas
The MLOps Stack Canvas scope is to assist you while identifying the workflows, architecture, and infrastructure components for the MLOps stack in the ML project
- MLOps: Motivation
MLOps, like DevOps, emerges from the understanding that separating the ML model development from the process that delivers it — ML operations — lowers quality, transparency, and agility of the whole intelligent software The Evolution of the MLOps
- ML Model Governace
MLOps is equivalent to DevOps in software engineering: it is an extension of DevOps for the design, development, and sustainable deployment of ML models in software systems Model Governance encompasses a set of processes and frameworks that help in the deployment of ML
- End-to-end Machine Learning Workflow - ML Ops
Machine Learning Operations An Overview of the End-to-End Machine Learning Workflow In this section, we provide a high-level overview of a typical workflow for machine learning-based software development Generally, the goal of a machine learning project is to build a statistical model by using collected data and applying machine learning algorithms to them Therefore, every ML-based software
- Three Levels of ML Software
Machine Learning Model Operationalization Management - MLOps, as a DevOps extension, establishes effective practices and processes around designing, building, and deploying ML models into production
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