Agile Software Engineering Methodology for Information Systems’ Integration Projects

This chapter discusses how to build production-ready machine learning systems. There are several challenges involved in accomplishing this, each with its specific solutions regarding practices and tool support. The chapter presents those solutions and introduces MLOps (machine learning operations, also called machine learning engineering) as an overarching and integrated approach in which data engineers, data scientists, software engineers, and operations engineers integrate their activities to implement validated machine learning applications managed from initial idea to daily operation in a production environment. This approach combines agile software engineering processes with the machine learning-specific workflow. Following the principles of MLOps is paramount in building high-quality production-ready machine learning systems. The current state of MLOps is discussed in terms of best practices and tool support. The chapter ends by describing future developments that are bound to improve and extend the tool support for implementing an MLOps approach.

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Information Systems Integration to Enhance Operational Customer Relationship Management in the Pharmaceutical Industry

Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021) - Advances in Intelligent Systems and Computing ◽

10.1007/978-3-030-76346-6_50 ◽

2021 ◽

pp. 553-572

Author(s):

Petr Svoboda ◽

Taher M. Ghazal ◽

Mohammed A. M. Afifi ◽

Deepak Kalra ◽

Muhammad T. Alshurideh ◽

...

Keyword(s):

Information Systems ◽

Pharmaceutical Industry ◽

Customer Relationship Management ◽

Relationship Management ◽

Systems Integration ◽

Customer Relationship ◽

Information Systems Integration

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Qualitative Analysis of Semantically Enabled Knowledge Management Systems in Agile Software Engineering

Conceptual Models and Outcomes of Advancing Knowledge Management ◽

10.4018/978-1-4666-0035-5.ch008 ◽

2012 ◽

pp. 144-164

Author(s):

Jörg Rech ◽

Christian Bogner

Keyword(s):

Knowledge Management ◽

Software Engineering ◽

Qualitative Analysis ◽

Management Systems ◽

Knowledge Management Systems ◽

Two Phase ◽

Complex Processes ◽

Management Processes ◽

Agile Software Engineering ◽

Agile Software

In many agile software engineering organizations there is not enough time to follow knowledge management processes, to retrieve knowledge in complex processes, or to systematically elicit knowledge. This chapter gives an overview about the human-centered design of semantically-enabled knowledge management systems based on Wikis used in agile software engineering environments. The methodology – developed in the RISE (Reuse in Software Engineering) project – enables and supports the design of human-centered knowledge sharing platforms, such as Wikis. Furthermore, the paper specifies requirements one should keep in mind when building human-centered systems to support knowledge management. A two-phase qualitative analysis showed that the knowledge management system acts as a flexible and customizable view on the information needed during working-time which strongly relieves software engineers from time-consuming retrieval activities. Furthermore, the observations gave some hints about how the software system supports the collection of vital working experiences and how it could be subsequently formed and refined.

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