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
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 ...
An Overview of the End-to-End Machine Learning Workflow - ML …
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.
ML Model Governace - ML Ops
Setting up automatized and reproducible data and ML pipelines reduces the amount of time required to bring models into production (time-to-market). There are six interactive phases in the ML development process: Business and Data Understanding. Data Engineering. Model Engineering. Quality Assurance for ML Systems.
MLOps References
An Introduction to Machine Learning Interpretability, by Patrick Hall, Navdeep Gill, 2nd Edition. O’Reilly 2019. Examples of techniques for training interpretable machine learning (ML) models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
CRISP-ML(Q). The ML Lifecycle Process. - ML Ops
Overall, CRISP-ML(Q) is a systematic process model for machine learning software development that creates an awareness of possible risks and emphasizes quality assurance to diminish these risks to ensure the ML project’s success.
MLOps Stack Canvas
To specify an architecture and infrastructure stack for Machine Learning Operations, we suggest a general MLOps Stack Canvas framework designed to be application- and industry-neutral. We align to the CRISP-ML(Q) model and describe the eleven components of the MLOps stack and line them up along with the ML Lifecycle and the “AI Readiness ...
Why you Might Want to use Machine Learning - ML Ops
Why you Might Want to use Machine Learning. According to Statista Digital Economy Compass 2019, two major trends will disrupt the economy and our lives: Data-driven world, which is linked to the exponentially-growing amount of digitally-collected data. The increasing importance of Artificial Intelligence / Machine Learning / Data Science, which ...
Three Levels of ML Software - ML Ops
We describe here essential technical methodologies, which are involved in the development of the Machine Learning-based software, namely Data Engineering, ML Model Engineering, and Software Release Engineering. We recommend documenting everything you have learned in each step of the whole pipeline.
MLOps: Phase Zero
The AI Canvas or its alternative, the Machine Learning Canvas, assist and help to structure the breakdown process. They also help to articulate exactly what is needed to predict and how we react on errors made by the prediction algorithm.