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 development 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 communities. This survey presents a recent time-slide comprehensive overview with comparisons as well as trends in development and usage of cutting-edge Artificial 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.

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.

The work presented in this paper deals with a proactive network monitoring for security and protection of computing infrastructures. We provide an exploitation of an intelligent module, in the form of a as a machine learning application using deep learning modeling, in order to enhance functionality of intrusion detection system supervising network traffic flows. Currently, intrusion detection systems work well for network monitoring in near real-time and they effectively deal with threats in a reactive way. Deep learning is the emerging generation of artificial intelligence techniques and one of the most promising candidates for intelligence integration into traditional solutions leading to quality improvement of the original solutions. The work presented in this paper faces the challenge of cooperation between deep learning techniques and large-scale data processing. The outcomes obtained from extensive and careful experiments show the applicability and feasibility of simultaneously modelled multiple monitoring channels using deep learning techniques. The proper joining of deep learning modelling with scalable data preprocessing ensures high quality and stability of model performance in dynamic and fast-changing environments such as network traffic flow monitoring.

In this paper, a novel algorithm called opposition-based coral reefs optimization (OCRO) is introduced. The algorithm is built as an improvement for coral reefs optimization (CRO) using opposition-based learning (OBL). For efficient modeling as the main part of this work, a novel time series forecasting model called OCRO-multi-layer neural network (MLNN) is proposed to explore hidden relationships in the non-linear time series data. The model thus combines OCRO with MLNN for data processing, which enables reducing the model complexity by faster convergence than the traditional back-propagation algorithm. For validation of the proposed model, three real-world datasets are used, including Internet traffic collected from a private internet service provider (ISP) with distributed centers in 11 European cities, WorldCup 98 contains request numbers to the server in football world cup season in 1998, and Google cluster log dataset gathered from its data center. Through the carried out experiments, we demonstrated that with both univariate and multivariate data, the proposed prediction model gains good performance in accuracy, run time and model stability aspects as compared with other modern learning techniques like recurrent neural network (RNN) and long short-term memory (LSTM). In addition, with used real datasets, we intend to concentrate on applying OCRO-MLNN to distributed systems in order to enable the proactive resource allocation capability for e-infrastructures (e.g. clouds services, Internet of Things systems, or blockchain networks).

As the field of machine learning advances, managing and monitoring intelligent models in production, also known as machine learning operations (MLOps), has become essential. Organizations are increasingly adopting artificial intelligence as a strategic tool, thus increasing the need for reliable, and scalable MLOps platforms. Consequently, every aspect of the machine learning life cycle, from workflow orchestration to performance monitoring, presents both challenges and opportunities that require sophisticated, flexible, and scalable technological solutions. This research addresses this demand by providing a comprehensive assessment framework of MLOps platforms highlighting the key features necessary for a robust MLOps solution. The paper examines 16 MLOps tools widely used, which revolve around capabilities within AI infrastructure management, including but not limited to experiment tracking, model deployment, and model inference. Our three-step evaluation framework starts with a feature analysis of the MLOps platforms, then GitHub stars growth assessment for adoption and prominence, and finally, a weighted scoring method to single out the most influential platforms. From this process, we derive valuable insights into the essential components of effective MLOps systems and provide a decision-making flowchart that simplifies platform selection. This framework provides hands-on guidance for organizations looking to initiate or enhance their MLOps strategies, whether they require an end-end solutions or specialized tools.