Machine learning operations (mlops): Overview, definition, and architecture
D Kreuzberger, N Kühl, S Hirschl - IEEE access, 2023 - ieeexplore.ieee.org
The final goal of all industrial machine learning (ML) projects is to develop ML products and
rapidly bring them into production. However, it is highly challenging to automate and …
rapidly bring them into production. However, it is highly challenging to automate and …
Towards mlops: A framework and maturity model
The adoption of continuous software engineering practices such as DevOps (Development
and Operations) in business operations has contributed to significantly shorter software …
and Operations) in business operations has contributed to significantly shorter software …
[HTML][HTML] Democratizing artificial intelligence: How no-code AI can leverage machine learning operations
Organizations are increasingly seeking to generate value and insights from their data by
integrating advances in artificial intelligence (AI)(eg, machine learning (ML) systems) into …
integrating advances in artificial intelligence (AI)(eg, machine learning (ML) systems) into …
Mlops-definitions, tools and challenges
This paper is an concentrated overview of the Machine Learning Operations (MLOps) area.
Our aim is to define the operation and the components of such systems by highlighting the …
Our aim is to define the operation and the components of such systems by highlighting the …
Mlops: A review
Recently, Machine Learning (ML) has become a widely accepted method for significant
progress that is rapidly evolving. Since it employs computational methods to teach machines …
progress that is rapidly evolving. Since it employs computational methods to teach machines …
[HTML][HTML] The pipeline for the continuous development of artificial intelligence models—Current state of research and practice
Companies struggle to continuously develop and deploy Artificial Intelligence (AI) models to
complex production systems due to AI characteristics while assuring quality. To ease the …
complex production systems due to AI characteristics while assuring quality. To ease the …
An adaptable and unsupervised TinyML anomaly detection system for extreme industrial environments
Industrial assets often feature multiple sensing devices to keep track of their status by
monitoring certain physical parameters. These readings can be analyzed with machine …
monitoring certain physical parameters. These readings can be analyzed with machine …
[HTML][HTML] Systematic review of data-centric approaches in artificial intelligence and machine learning
P Singh - Data Science and Management, 2023 - Elsevier
Artificial intelligence (AI) relies on data and algorithms. State-of-the-art (SOTA) AI smart
algorithms have been developed to improve the performance of AI-oriented structures …
algorithms have been developed to improve the performance of AI-oriented structures …
From DevOps to MLOps: Overview and application to electricity market forecasting
In the Software Development Life Cycle (SDLC), Development and Operations (DevOps)
has been proven to deliver reliable, scalable software within a shorter time. Due to the …
has been proven to deliver reliable, scalable software within a shorter time. Due to the …
What drives MLOps adoption? An analysis using the TOE framework
SD Das, PK Bala - Journal of Decision Systems, 2024 - Taylor & Francis
MLOps is essential to streamline the machine learning (ML) development process, ensure
ML models stay operational, and provide users with the desired value. MLOps enhances the …
ML models stay operational, and provide users with the desired value. MLOps enhances the …