- ML Ops: 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
In the following, we describe a set of important concepts in MLOps such as Iterative-Incremental Development, Automation, Continuous Deployment, Versioning, Testing, Reproducibility, and Monitoring
- 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
- State of MLOps
This template breaks down a machine learning workflow into nine components, as described in the MLOps Principles Before selecting tools or frameworks, the corresponding requirements for each component need to be collected and analysed
- MLOps Stack Canvas
To specify an architecture and infrastructure stack for Machine Learning Operations, we reviewed the CRISP-ML (Q) development lifecycle and suggested an application- and industry-neutral MLOps Stack Canvas
- MLOps: Motivation
The term MLOps is defined as “the extension of the DevOps methodology to include Machine Learning and Data Science assets as first-class citizens within the DevOps ecology” Source: MLOps SIG
- CRISP-ML (Q)
Machine Learning OperationsCRISP-ML (Q) The ML Lifecycle Process The machine learning community is still trying to establish a standard process model for machine learning development As a result, many machine learning and data science projects are still not well organized Results are not reproducible In general, such projects are conducted in an ad-hoc manner To guide ML practitioners
- End-to-end Machine Learning Workflow - ML Ops
Machine Learning OperationsAn 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
|