The growth in the number of IT products with machine-learning features is increasing the relevance of automating machine-learning processes. The use of MLOps techniques is aimed at providing training and efficient deployment of applications in a production environment by automating side infrastructure issues that are not directly related to model development.

In this paper, we review the components, principles, and approaches of MLOps and analyze existing platforms and solutions for building machine learning pipelines. In addition, we propose an approach to build a machine learning pipeline based on basic DevOps tools and open-source libraries.

The COVID-19 pandemic makes the problem of monitoring and analyzing the movement of people indoors more urgent in order to timely identify those who have been in contact with the sick and prevent further spread of the infection.

The article proposes one of the ways to solve this problem - the development of a system for determining and saving the history of the location of people inside the premises. The article also discusses methods, parameters and technologies that can be used to solve the problem of indoor localization.

Cloud platforms and services have become an important factor in the explosive development of the digital economy in the last decade. The ability to quickly scale the service, coupled with a reduction in investment costs at the start of projects within the framework of the Iaas, PaaS, SaaS approaches, gave positive results and formed the basis of new business models and corporate solutions.

In this article, we discuss the reasons for the importance of multicloud and explore approaches to integrating cloud services in a multicloud architecture. The article also proposes a way to solve the problem of cloud migration - developing a system for migrating cloud resources between cloud services.

In the context of ever-increasing informatization, automation and digitalization of business, new schemes of unfair actions by both legal entities and individuals are emerging. In this regard, there is an acute problem of quick, effective and high-quality identification of information about a potential or current counterparty, which will allow you to quickly make the right management decisions.

The article describes one of the ways to solve this problem - the development of a system of information monitoring of counterparties, which will allow you to quickly identify and analyze information about their activities.

This paper addresses the issue of automatic annotation of training datasets in the field of computer vision using machine learning methods. Data annotation is a key stage in the development and training of deep learning models, yet the process of creating labeled data often requires significant time and labor. This paper proposes a mechanism for automatic annotation based on the use of convolutional neural networks (CNN) and active learning methods.

The proposed methodology includes the analysis and evaluation of existing approaches to automatic annotation. The effectiveness of the proposed solutions is assessed on publicly available datasets. The results demonstrate that the proposed method significantly reduces the time required for data annotation, although operator intervention is still necessary.

The literature review includes an analysis of modern annotation methods and existing automatic systems, providing a better understanding of the context and advantages of the proposed approach. The conclusion discusses achievements, limitations, and possible directions for future research in this field.

Model robustness to minor deviations in the distribution of input data is an important criterion in many tasks. Neural networks show high accuracy on training samples, but the quality on test samples can be dropped dramatically due to different data distributions, a situation that is exacerbated at the subgroup level within each category. In this article we show how the robustness of the model at the subgroup level can be significantly improved with the help of the domain adaptation approach to image embeddings. We have found that application of a competitive approach to embeddings limitation gives a significant increase of accuracy metrics in a complex subgroup in comparison with the previous models. The method was tested on two independent datasets, the accuracy in a complex subgroup on the Waterbirds dataset is 90.3 {y : waterbirds;a : landbackground}, on the CelebA dataset is 92.22 {y : blondhair;a : male}.

The article is devoted to the creation of an effective solution for user segmentation. The article presents an analysis of existing user segmentation services, an analysis of approaches to user segmentation (ABCDx segmentation, demographic segmentation, segmentation based on a user journey map), an analysis of clustering algorithms (K-means, Mini-Batch K-means, DBSCAN, Agglomerative Clustering, Spectral Clustering). The study of these areas is aimed at creating a “flexible” segmentation solution that adapts to each user sample. Dispersion analysis (ANOVA test), analysis of clustering metrics is also used to assess the quality of user segmentation. With the help of these areas, an effective solution for user segmentation has been developed using advanced analytics and machine learning technology.

The article is devoted to automation of the software design stage. In the course of the study, the reasons for the high importance of this stage and the relevance of its automation were analyzed. The main stages of this stage were also considered and the existing systems that allow automating each of them were considered. In addition, an own solution was proposed within the framework of the problem of class structure refactoring based on the combinatorial optimization method. A solution method has been developed to improve the quality of the class hierarchy and tested on a real model.