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 …
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, …
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 …
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 Team
MLOps Team at INNOQ Dr. Larysa Visengeriyeva Anja Kammer Isabel Bär Alexander Kniesz Advisory Board Michael Plöd (DDD Advisor)
MLOps References
MLOps: Model management, deployment and monitoring with Azure Machine Learning Guide to File Formats for Machine Learning: Columnar, Training, Inferencing, and the Feature Store
MLOps: Phase Zero
The most important phase in any software project is to understand the business problem and create requirements. ML-based software is no different here. The initial step includes a …
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 …
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.
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 …