Bringing a Machine Learning Based Novelty Detection Software Tool from Research to Production

This paper presents the process of bringing a machine learning based novelty detection software tool from research to production. Moreover, it sums up the necessary changes that needed to be done for developing a scientific software library into a software product with an application in space operations. This process considers the needs and expectations of all stakeholders. The system for which this process is shown is the Automated Telemetry Health Monitoring System (ATHMoS) developed at the German Space Operations Center of the German Aerospace Center. In its early phase as a research software, it paved the way for the novelty detection research. After its value for the satellite engineer’s daily work became visible, it evolved to a robust and resilient software tool that can be used in a productive environment to support the engineers in their routine work. Furthermore, the integration of the system into our Visualization and Data Analysis framework is explained. This framework has a web-based front-end for the interactive exploration and analysis of satellite telemetry data

Automated deployment of machine learning applications to the cloud

The use of machine learning (ML) as a key technology in artificial intelligence (AI) is becoming more and more important in the increasing digitalization of business processes. However, the majority of the development effort of ML applications is not related to the programming of the ML model, but to the creation of the server structure, which is responsible for a highly available and error-free productive operation of the ML application. The creation of such a server structure by the developers is time-consuming and complicated, because extensive configurations have to be made. Besides the creation of the server structure, it is also useful not to put new ML application versions directly into production, but to observe the behavior of the ML application with respect to unknown data for quality assurance. For example, the error rate as well as the CPU and RAM consumption should be checked. The goal of this thesis is to collect requirements for a suitable server structure and an automation mechanism that generates this server structure, deploys the ML application and allows to observe the behavior of a new ML application version based on real-time user data. For this purpose, a systematic literature review is conducted to investigate how the behavior of ML applications can be analyzed under the influence of real-time user data before their productive operation. Subsequently, in the context of the requirements analysis, a target-performance analysis is carried out in the department of a management consulting company in the automotive sector. Together with the results of the literature research, a list of user stories for the automation tool is determined and prioritized. The automation tool is implemented in the form of a Python console application that enables the desired functionality by using IaC (Infrastructure as code) and the AWS (Amazon Web Services) SDK in the cloud. The automation tool is finally evaluated in the department. The ten participants independently carry out predefined usage scenarios and then evaluate the tool using a questionnaire developed on the basis of the TAM model. The results of the evaluation are predominantly positive and the constructive feedback of the participants includes numerous interesting comments on possible adaptions and extensions of the automation tool

MLOps - Standardizing the Machine Learning Workflow

MLOps is a very recent approach aimed at reducing the time to get a Machine Learning model in production; this methodology inherits its main features from DevOps and applies them to Machine Learning, by adding more features specific for Data Analysis. This thesis, which is the result of the internship at Data Reply, is aimed at studying this new approach and exploring different tools to build an MLOps architecture; another goal is to use these tools to implement an MLOps architecture (by using preferably Open Source software). This study provides a deep analysis of MLOps features, also compared to DevOps; furthermore, an in-depth survey on the tools, available in the market to build an MLOps architecture, is offered by focusing on Open Source tools. The reference architecture, designed adopting an exploratory approach, is implemented through MLFlow, Kubeflow, BentoML and deployed by using Google Cloud Platform; furthermore, the architecture is compared to different use cases of companies that have recently started adopting MLOps. MLOps is rapidly evolving and maturing, for these reasons many companies are starting to adopt this methodology. Based on the study conducted with this thesis, companies dealing with Machine Learning should consider adopting MLOps. This thesis can be a starting point to explore MLOps both theoretically and practically (also by relying on the implemented reference architecture and its code)