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Engineering framework for scalable machine learning operations

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Moloney, Seamus
dc.contributor.author Scotton, Luca
dc.date.accessioned 2021-01-31T18:11:27Z
dc.date.available 2021-01-31T18:11:27Z
dc.date.issued 2021-01-25
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/102493
dc.description.abstract With the evolution of algorithms and solutions in the artificial intelligence field, new and modern methods and practices are required to successfully leverage these technologies. Therefore, a new field named Machine Learning Operations (MLOps) has grown rapidly in the last five years. The goal is to increase integration between the pure research in the artificial intelligence domain and the traditional software engineering domain to generate business value faster, by rapidly shifting machine learning algorithms to the production stage. The thesis aims to provide a novel machine leaning framework to exploit the business potential of solutions to artificial intelligence and data mining problems in the industry by introducing novel tools and practices, which ensure automation and maintenance are guaranteed on the long run. In particular, the thesis introduces the Model-as-a-Package concept. The design considers a machine learning pipeline as a highly specialized package. Based on this, a novel framework is proposed and implemented. The ultimate goal is to enable robust automation in the model versioning, model deployment and monitoring aspects of machine learning operations. Compared to the state-of-the-art end-to-end open source frameworks in the market, the features the framework introduces outperform the alternatives in the context of model life cycle processes. In particular, major improvements have been proposed for dependency management, automated logging and automatic model update. Moreover, the framework improves the state of the art by decoupling logic into two highly integrated components, namely Trainer and Predictor. This expands the set of inference services supported by the framework by adding low resources serving channels such as serverless functions and IoT devices. en
dc.format.extent 77
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.title Engineering framework for scalable machine learning operations en
dc.type G2 Pro gradu, diplomityö fi
dc.contributor.school Perustieteiden korkeakoulu fi
dc.subject.keyword machine learning operations en
dc.subject.keyword framework en
dc.subject.keyword data science en
dc.subject.keyword devops en
dc.subject.keyword cybersecurity en
dc.subject.keyword serverless en
dc.identifier.urn URN:NBN:fi:aalto-202101311796
dc.programme.major Data Science fi
dc.programme.mcode SCI3095 fi
dc.type.ontasot Master's thesis en
dc.type.ontasot Diplomityö fi
dc.contributor.supervisor Truong, Hong-Linh
dc.programme Master's Programme in ICT Innovation fi
local.aalto.electroniconly yes
local.aalto.openaccess yes

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