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The combined impact of new computing resources and techniques with an increasing avalanche of large datasets, is transforming many research areas and may lead to technological breakthroughs that can be used by billions of people. In the recent years, machine Learning and especially its subfield Deep Learning have seen impressive advances. Techniques developed within these two fields are now able to analyze and learn from huge amounts of real world examples in a disparate formats. While the number of Machine Learning algorithms is extensive and growing, their implementations through frameworks and libraries is also extensive and growing too. The software devel- opment in this field is fast paced with a large number of open-source software coming from the academy, industry, start-ups or wider open-source commu- nities. This survey presents a recent time-slide comprehensive overview with comparisons as well as trends in development and usage of cutting-edge Arti- ficial Intelligence software. It also provides an overview of massive parallelism support that is capable of scaling computation effectively and efficiently in the era of Big Data. ; Álvaro López García, Ignacio Heredia, Giang Nguyen, Viet Tran, Stefan Dlugolinsky, Martin Bobák, and Ladislav Hluchý are supported by the project DEEP-HybridDataCloud "Designing and Enabling E-infrastructures for intensive Processing in a Hybrid DataCloud" that has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 777435. Giang Nguyen, Viet Tran, Stefan Dlugolinsky, Martin Bobák, and Ladislav Hluchý are also supported by the Project VEGA 2/0167/16 "Methods and algorithms for the semantic processing of Big Data in distributed computing environment". The authors would like to thanks to all colleagues, especially for Ján Astaloš for knowledge sharing and teamwork. ; Peer reviewed

[EN] In this paper we propose a distributed architecture to provide machine learning practitioners with a set of tools and cloud services that cover the whole machine learning development cycle: ranging from the models creation, training, validation and testing to the models serving as a service, sharing and publication. In such respect, the DEEP-Hybrid-DataCloud framework allows transparent access to existing e-Infrastructures, effectively exploiting distributed resources for the most compute-intensive tasks coming from the machine learning development cycle. Moreover, it provides scientists with a set of Cloud-oriented services to make their models publicly available, by adopting a serverless architecture and a DevOps approach, allowing an easy share, publish and deploy of the developed models. ; This work was supported by the project DEEP-Hybrid-DataCloud ``Designing and Enabling E-infrastructures for intensive Processing in a Hybrid DataCloud'' that has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant 777435 ; Lopez Garcia, A.; Marco De Lucas, J.; Antonacci, M.; Zu Castell, W.; David, M.; Hardt, M.; Lloret Iglesias, L. (2020). A Cloud-Based Framework for Machine Learning Workloads and Applications. IEEE Access. 8:18681-18692. https://doi.org/10.1109/ACCESS.2020.2964386 ; S ; 18681 ; 18692 ; 8