This work deals with the concept of DevOps and its application in the improvement of machine learning processes. DevOps practices have been increasingly used by software development teams to automate and simplify processes ranging from integration, through testing, approval, implementation, to the final delivery of the application to users. The present study aims to focus on the possibility of applying this concept also in teams that work with machine learning and could benefit from the improvements brought with the adoption of DevOps
Companies follow different approaches in the life cycle of software, but usually, activities are div...
Summarization: Machine Learning (ML) represents an advanced technology and its effective implementat...
Context: DevOps is considered important in the ability to frequently and reliably update a system in...
When developing software systems that contain Machine Learning (ML) based components, the developmen...
The main purpose of this paper is to review DevOps theoretical framework using the machine learning ...
The main purpose of this paper is to review DevOps theoretical framework using the machine learning ...
Software and information systems have become a core competency for every business in this connected ...
This chapter discusses how to build production-ready machine learning systems. There are several cha...
Nowadays, machine learning projects have become more and more relevant to various real-world use cas...
Integrating machine learning components in software systems is a task more and more companies are co...
The adoption of continuous software engineering practices such as DevOps (Development and Operations...
The thesis was carried out at a company that works with large financial institutions that have start...
The maturing capabilities of Artificial Intelligence (AI) and Machine Learning (ML) have resulted in...
Background. Since the rise of Machine Learning, the automation of software development has been a de...
DevOps is the form of software engineering practices for software used to have better collaboration ...
Companies follow different approaches in the life cycle of software, but usually, activities are div...
Summarization: Machine Learning (ML) represents an advanced technology and its effective implementat...
Context: DevOps is considered important in the ability to frequently and reliably update a system in...
When developing software systems that contain Machine Learning (ML) based components, the developmen...
The main purpose of this paper is to review DevOps theoretical framework using the machine learning ...
The main purpose of this paper is to review DevOps theoretical framework using the machine learning ...
Software and information systems have become a core competency for every business in this connected ...
This chapter discusses how to build production-ready machine learning systems. There are several cha...
Nowadays, machine learning projects have become more and more relevant to various real-world use cas...
Integrating machine learning components in software systems is a task more and more companies are co...
The adoption of continuous software engineering practices such as DevOps (Development and Operations...
The thesis was carried out at a company that works with large financial institutions that have start...
The maturing capabilities of Artificial Intelligence (AI) and Machine Learning (ML) have resulted in...
Background. Since the rise of Machine Learning, the automation of software development has been a de...
DevOps is the form of software engineering practices for software used to have better collaboration ...
Companies follow different approaches in the life cycle of software, but usually, activities are div...
Summarization: Machine Learning (ML) represents an advanced technology and its effective implementat...
Context: DevOps is considered important in the ability to frequently and reliably update a system in...