Academic literature on the topic 'Google cloud projects'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Google cloud projects.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Google cloud projects"
1
Zulpukarova, D. "Google Applications in Student’s Self-learning." Bulletin of Science and Practice 5, no. 12 (2019): 420–30. http://dx.doi.org/10.33619/2414-2948/49/52.
Full textAbstract:
Problem and rationale. Despite the spread of information technology in various areas of the modern society, the possibilities of cloud technology are not used much in the practice of university education. The article substantiates the effectiveness of the use of Google applications in the process of organizing student’s self-learning. Methodology. While researching the problem, we have used the analysis of theoretical and methodological literature, study of best practices in teaching mathematics and computer science using information technology, analysis of the advantages and disadvantages of using cloud technology in training, conduct of computer science courses using Google applications, as well as the management of independent learning of first-year students of Osh State University using cloud technology. Results. The capabilities of the specialized services of Google.docs, Google.tables, Google.presentation and Google.forms for creating a document, presentation, spreadsheet, online questionnaires and online tests have been defined. The use of Google services in the practice of training leads to the formation of student’s training in the skills of working with cloud technologies, meanwhile students demonstrate three levels of knowledge of cloud technologies: high, medium and low. Conclusions. Working with cloud technologies, it is possible for students to build ICT skills, skills of self-organization. Using Google applications, it is potential to monitor the implementation of self-learning, student projects, both in the process of completing assignments and achieving final results, the outcome of which is the improvement of teacher’s work process and saving time.
2
Barreiro Megino, Fernando, Mikhail Borodin, Kaushik De, et al. "Accelerating science: The usage of commercial clouds in ATLAS Distributed Computing." EPJ Web of Conferences 295 (2024): 07002. http://dx.doi.org/10.1051/epjconf/202429507002.
Full textAbstract:
The ATLAS experiment at CERN is one of the largest scientific machines built to date and will have ever growing computing needs as the Large Hadron Collider collects an increasingly larger volume of data over the next 20 years. ATLAS is conducting R&D projects on Amazon Web Services and Google Cloud as complementary resources for distributed computing, focusing on some of the key features of commercial clouds: lightweight operation, elasticity and availability of multiple chip architectures. The proof of concept phases have concluded with the cloud-native, vendoragnostic integration with the experiment’s data and workload management frameworks. Google Cloud has been used to evaluate elastic batch computing, ramping up ephemeral clusters of up to O(100k) cores to process tasks requiring quick turnaround. Amazon Web Services has been exploited for the successful physics validation of the Athena simulation software on ARM processors. We have also set up an interactive facility for physics analysis allowing endusers to spin up private, on-demand clusters for parallel computing with up to 4 000 cores, or run GPU enabled notebooks and jobs for machine learning applications. The success of the proof of concept phases has led to the extension of the Google Cloud project, where ATLAS will study the total cost of ownership of a production cloud site during 15 months with 10k cores on average, fully integrated with distributed grid computing resources and continue the R&D projects.
3
Wilson, Deirdre. "A Collaborative Story Writing Project Using Google Docs and Face-to-Face Collaboration." Canadian Journal of Learning and Technology 49, no. 3 (2025): 1–21. https://doi.org/10.21432/cjlt28174.
Full textAbstract:
The Google Docs application is part of Google Workspace for Education, a suite of cloud-based productivity and collaboration tools that are now ubiquitous in middle and high school classrooms. While there is an expanding body of research documenting the benefits of using Google Docs to support collaborative writing projects, there exists few qualitative studies detailing how cloud-based tools are integrated into courses that meet face-to-face on an ongoing basis. This case study explores how an experienced high school English teacher facilitated a collaborative writing project, in which students used Google Docs to co-write a story. The students were instructed to work on their stories asynchronously from home and synchronously during face-to-face classes. Data sources included field notes from class observations, reflections written by the teacher, semi-structured interviews with the teacher, focus group interviews with the students, and the shared Google Docs. This article describes affordances and constraints associated with the pedagogical supports provided during the collaborative writing process and offers recommendations for teachers who intend to use Google Docs to facilitate collaborative writing projects.
4
Atri, Preyaa. "Enabling AI Workflows: A Python Library for Seamless Data Transfer between Elasticsearch and Google Cloud Storage." Journal of Artificial Intelligence, Machine Learning and Data Science 1, no. 1 (2022): 489–91. https://doi.org/10.51219/JAIMLD/preyaa-atri/132.
Full textAbstract:
This paper introduces a Python library designed to accelerate AI workflows by facilitating seamless data transfer between Elasticsearch, a powerful search engine for unstructured data, and Google Cloud Storage (GCS), a scalable cloud storage platform. By automating the migration of large datasets from Elasticsearch to GCS, the library empowers AI researchers and practitioners to efficiently leverage cloud-based resources for model training, preprocessing, and analysis. This research delves into the library's features, dependencies, usage patterns, and its potential to enhance data management efficiency in AI-driven projects. Additionally, the paper discusses the library's limitations and proposes future enhancements to further streamline AI development pipelines.
5
Khan, Nawsher, A. Noraziah, Elrasheed I. Ismail, Mustafa Mat Deris, and Tutut Herawan. "Cloud Computing." International Journal of E-Entrepreneurship and Innovation 3, no. 2 (2012): 51–59. http://dx.doi.org/10.4018/jeei.2012040104.
Full textAbstract:
Cloud computing is fundamentally altering the expectations for how and when computing, storage, and networking resources should be allocated, managed, consumed, and allow users to utilize services globally. Due to the powerful computing and storage, high availability and security, easy accessibility and adaptability, reliable scalability and interoperability, cost and time effective cloud computing is the top, needed for current fast growing business world. A client, organization or a trade that adopting emerging cloud environment can choose a well suitable infrastructure, platform, software, and a network resource, for any business, where each one has some exclusive features and advantages. The authors first develop a comprehensive classification for describing cloud computing architecture. This classification help in survey of several existing cloud computing services developed by various projects globally such as Amazon, Google, Microsoft, Sun and Force.com and by using this survey’s results the authors identified similarities and differences of the architecture approaches of cloud computing.
6
Osadcha, Kateryna P., and Viacheslav V. Osadchyi. "The use of cloud computing technology in professional training of future programmers." CTE Workshop Proceedings 8 (March 19, 2021): 155–64. http://dx.doi.org/10.55056/cte.229.
Full textAbstract:
The article provides a brief analysis of the current state of the study of cloud technologies by future software engineers at foreign and Ukrainian universities. The author experience in the application of cloud technologies in the training of future software engineers in Ukraine is presented. The application of cloud business automation systems, online services to monitor the implementation of the software projects, Google services for collaboration, planning and productivity while studying professional disciplines and carrying out diploma projects is described. Based on the survey conducted at Stackoverflow, the state of application of cloud technologies by software engineers around the world has been analyzed. The cloud technologies that are not studied at the analyzed universities of Ukraine and those that are not popular with software developers in the world, but studied at Ukrainian universities by future software engineers are outlined. Conclusions are made on the modernization of training programs for future software engineers. Topics for the study of cloud technologies by future software engineers in the content of professional disciplines are proposed.
7
Bhargav, Bachina. "Deploying Python APIs on GCP GKE with HELM: A Comprehensive Guide." European Journal of Advances in Engineering and Technology 8, no. 11 (2021): 28–38. https://doi.org/10.5281/zenodo.10901249.
Full textAbstract:
<strong>ABSTRACT</strong> Kubernetes has rapidly become a cornerstone technology in modern IT infrastructure, with widespread adoption across various industries. However, deploying applications on Kubernetes entails managing numerous objects such as deployments, configmaps, and secrets, each defined in manifest files. While this approach works for initial deployments, it becomes cumbersome for repeated deployments. Enter Helm, the Kubernetes package manager designed to simplify application deployment, enhance security, and provide configurability. Helm streamlines the deployment process by enabling users to package and manage Kubernetes applications efficiently. In this paper, we explore deploying Python APIs on Google Cloud Platform's Google Kubernetes Engine (GKE) using Helm, covering essential prerequisites, Dockerization, container image pushing, Helm chart creation, GKE cluster configuration, Helm chart installation, accessing the deployed API, cluster cleanup, and concluding insights. Through this exploration, we aim to provide a comprehensive guide for deploying applications on Kubernetes with Helm, empowering users to leverage the full potential of Kubernetes in their projects.
8
Venica, Liptia, Muhammad Ayyas, Muchamad Amin Maezun Ta'sin Billah, Rista Dewi Opsantini, and Syaifi Al-Mahfudzi. "Introduction to Cloud Computing Fundamental for Teachers and Students of SMK Negeri 1 Karangdadap Kabupaten Pekalongan." REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat 4, no. 3 (2023): 232–42. http://dx.doi.org/10.26760/rekaelkomika.v4i3.232-242.
Full textAbstract:
The use of cloud computing technology is currently increasingly widespread. The purpose of this community service is to introduce vocational high school students and teachers to cloud computing and implement it to solve computational problems. Participants are introduced to cloud architecture, the advantages of using cloud, various cloud service providers, and different categories of products that the cloud service providers deliver. During the training, participants are provided with the case studies on the use of three products available on Google Cloud Platform (GCP), i.e., Compute Engine, Cloud Storage, and Cloud Vision API. As a result, participants are able to create virtual machines using Compute Engine, publish static web pages using Cloud Storage, and create an application to detect the name and location of a landmark from an image using Cloud Vision API. After this activity, participants are expected to be able to explore other cloud products available and use them to create their own projects.
9
Kratzke, Nane. "Volunteer Down: How COVID-19 Created the Largest Idling Supercomputer on Earth." Future Internet 12, no. 6 (2020): 98. http://dx.doi.org/10.3390/fi12060098.
Full textAbstract:
From close to scratch, the COVID-19 pandemic created the largest volunteer supercomputer on earth. Sadly, processing resources assigned to the corresponding Folding@home project cannot be shared with other volunteer computing projects efficiently. Consequently, the largest supercomputer had significant idle times. This perspective paper investigates how the resource sharing of future volunteer computing projects could be improved. Notably, efficient resource sharing has been optimized throughout the last ten years in cloud computing. Therefore, this perspective paper reviews the current state of volunteer and cloud computing to analyze what both domains could learn from each other. It turns out that the disclosed resource sharing shortcomings of volunteer computing could be addressed by technologies that have been invented, optimized, and adapted for entirely different purposes by cloud-native companies like Uber, Airbnb, Google, or Facebook. Promising technologies might be containers, serverless architectures, image registries, distributed service registries, and all have one thing in common: They already exist and are all tried and tested in large web-scale deployments.
10
Karmana, Putu Dian, I. Putu Bagus Ambara Yasa, Gusti Komang Tri Wismana, and I Putu Buda Suyasa. "Rancangan Pengarsipan Laporan Digital pada Instansi Dinas Kebudayaan Provinsi Bali Berbasis Google Site." JIS SIWIRABUDA 2, no. 1 (2024): 11–16. https://doi.org/10.58878/jissiwirabuda.v2i1.282.
Full textAbstract:
The Design of Digital Report Archiving at the Cultural Office of Bali Province based on Google Site aims to enhance efficiency and accessibility in document management. Leveraging the Google Site platform, this design integrates cloud-based data storage with collaborative features, facilitating the archiving and retrieval processes. Digital report documents, encompassing cultural activities and related projects, can be swiftly uploaded, organized, and accessed through a user-friendly interface. Data security is ensured through well-managed access rights. With the implementation of this design, the Cultural Office of Bali Province anticipates optimizing operational effectiveness, reducing paper usage, and fostering collaboration among team members in the management of digital reports.
Dissertations / Theses on the topic "Google cloud projects"
1
Koudelka, Jan. "Nástroj pro správu projektů s využitím tabletů platformy Android." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236073.
Full textAbstract:
This thesis is dedicated to the issue of project management and its support using software tools. It is focused on analysis of existing solutions and designing a new mobile application for Android platform. It summarizes basic principles of making mobile applications for Android OS. Designed application uses some properties of cloud computing for scaling and synchronizing data easily.
2
Talevi, Iacopo. "Big Data Analytics and Application Deployment on Cloud Infrastructure." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14408/.
Full textAbstract:
This dissertation describes a project began in October 2016. It was born from the collaboration between Mr.Alessandro Bandini and me, and has been developed under the supervision of professor Gianluigi Zavattaro. The main objective was to study, and in particular to experiment with, the cloud computing in general and its potentiality in the data elaboration field.
Cloud computing is a utility-oriented and Internet-centric way of delivering IT services on demand.
The first chapter is a theoretical introduction on cloud computing, analyzing the main aspects, the keywords, and the technologies behind clouds, as well as the reasons for the success of this technology and its problems. After the introduction section, I will briefly describe the three main cloud platforms in the market.
During this project we developed a simple Social Network. Consequently in the third chapter I will analyze the social network development, with the initial solution realized through Amazon Web Services and the steps we took to obtain the final version using Google Cloud Platform with its charateristics.
To conclude, the last section is specific for the data elaboration and contains a initial theoretical part that describes MapReduce and Hadoop followed by a description of our analysis. We used Google App Engine to execute these elaborations on a large dataset. I will explain the basic idea, the code and the problems encountered.
3
Semmler, Jiří. "Nástroj pro podporu spolupráce při agilním modelování a vývoji software." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2017. http://www.nusl.cz/ntk/nusl-363769.
Full textAbstract:
The ain of this thesis is to define and describe specific challenges occuring on the crossroard between project management and knowledge management with the focus on agile software development and agile modeling. Based on the found and verified problem it tries to find a existing solution. After that, it analyses, specifies and designs an own solution. Focusing on covering of three different perspectives makes this thesis unique. After process of design, there are technologies defined. For all used technologies there is described detailed implementation of the application. The third-party technologies are connected in this application.This connection creates the extra added value for the application and user in processes of agile software development and agile modeling.
4
Hsueh, Po-Jen, and 薛博仁. "An Implementation of a Cloud Project Management System on Google App Engine Platform." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/79642848150415952640.
Full textAbstract:
碩士<br>國立東華大學<br>資訊工程學系<br>103<br>With the economic globalization, the dependence of the software are increasing, and more widespread. It also highlights the importance of software project management. Migrating to cloud computing environments from conventional computing environments, the project management methods, tools, and knowledge are more important. There are some common problems in the traditional software project management, such as process delay, budget reduction, low quality of produced software, etc. In order to improve the productivity and quality of software, we need to simplify the development and deployment process. Cloud computing provides virtualized resources for users, through the applications platform via the internet. The software development in cloud computing environments provides more simplicity, flexibility and scalability for developers.
WebSD is a project new management model for distributed software development. It simplifies the conventional hierarchical architecture of distributed software development. WebSD extends conventional models to provide various views to the distributed software development for the roles involved, including project managers, software developers, software testers, and software debuggers, in order for all members to cooperate in a common and open model. Extended from WebSD, SDoT offers a common platform for various roles involved in the distributed software development, and includes specialized functionality for schedule control. In SDoT, the project manager can control the development schedule with automatic mechanism, so that the whole project can be developed more efficiently.
The project management system with automatic schedule control in cloud computing platform developed in this paper is based on Google App Engine technology to provide cloud service using cloud computing and the cloud storage. These technologies offer great benefits such as better usability, improved performance, greater scalability, which allow application interoperability among different systems. The Java programming environment provides a Java Servlet interface, supports standard interfaces and scalable datastore to the App Engine, such as Java Date Object. We implement the automatic schedule control mechanism using standard Java Server Pages technology, and build a SDoT-based distributed software project management system on Google App Engine platform, called as SDoT-GAE. Through the SDoT-GAE distributed software project management system, we expect to achieve real-time updates in the process of distributed software development at any time, and anywhere.
5
Chang, Wen-ying, and 張文瑩. "Project-based Cooperative Learning in Cloud Environments – A Case Study of Using Google Docs and Facebook as a Co-creation Platform." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/80589873978930252904.
Full textAbstract:
碩士<br>國立臺南大學<br>數位學習科技學系碩士班<br>100<br>In the past five years, numerous cloud systems or platforms, such as the Facebook or Google docs, have been applied in education situation called Cloud Learning because cloud computing have the advantage of reliable, customized, and QoS-guaranteed dynamic computing environments for end-users. Cloud learning provide teacher and students with collaborative creation platform and cooperating learning environment to enhance the relationship of bi-directional interaction in teacher-students and students-students.
This study adopted an innovative approach by combining two learning strategies (i.e. project-based learning and collaborative learning) in cloud learning platform to investigate the improvement of students’ learning performance and their satisfaction to this learning program. Furthermore, we adopted the Technology Acceptance Model (TAM) as a primary theoretical framework to investigate and prove the relationship among perceived ease of use (PEOU), perceived usefulness (PU), learning attitude (LA), and intention to use (ITU) from the viewpoint of students participating in project-based cooperative learning in cloud environments.
There were 120 university students in Taiwan participating in this research, and their ages ranged from 18 to 19. The participants of this experiment were randomly separated into two groups that include the experimental group and the control group. The control group using traditional teaching model, the experimental group implement the project-based cooperative learning in cloud environments. The period of the experiment was from November 2011 to January 2012 for eight weeks. After the learning activity was completed, all of the students took a post-test to measure their learning performance. Moreover, a TAM questionnaire and a satisfaction questionnaire were employed to measure participants’ attitude and satisfaction to this learning activity.
The results showed that (1) The learning performance of the experimental group was higher in the achievements of final test and report than the control group; (2) PEOU had a positive significant impact on PU; (3) PU had a positive significant impact on LA, whereas the influence of PEOU on LA was not significant; (4) PEOU, PE and LA makes a stronger positive impact on ITU for high-participating students, in which the influence of PE was greatest; (5) 70% participants satisfied the learning activity and the curriculum of the project-based cooperative learning in cloud environments after experiment activity.
Books on the topic "Google cloud projects"
1
Wickham, Mark. Practical Java Machine Learning: Projects with Google Cloud Platform and Amazon Web Services. Apress, 2018.
Find full text
2
Wickham. PRACTICAL JAVA MACHINE LEARNING: PROJECTS WITH GOOGLE CLOUD PLATFORM AND AMAZON WEB SERVICES [Paperback] Wickham. Apress, 2019.
Find full text
3
Google Drive for Businesses Guide: A Project Management and Work Collaboration Guide for Business Owners on Using Google Drive Tools and Storing Datain the Cloud with Illustrative Images. Independently Published, 2022.
Find full text
4
George, Noah. Google Drive for Businesses Guide: A Project Management and Work Collaboration Guide for Business Owners on Using Google Drive Tools and Storing Datain the Cloud with Illustrative Images. Independently Published, 2022.
Find full textBook chapters on the topic "Google cloud projects"
1
Szeto, Sabrina H. "Collaborating in Earth Engine with Scripts and Assets." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_28.
Full textAbstract:
AbstractMany users find themselves needing to collaborate with others in Earth Engine at some point. Students may need to work on a group project, people from different organizations might want to collaborate on research together, or people may want to share a script or an asset they created with others. This chapter will show you how to collaborate with others and share your work.
2
"Projects, Service Accounts, and Billing." In Google Cloud Certified Associate Cloud Engineer Study Guide. Wiley, 2019. http://dx.doi.org/10.1002/9781119564409.ch3.
Full text
3
Gopi, K., Anil Sharma, M. R. Jhansi Rani, et al. "Strategies to Achieve Carbon Neutrality and Foster Sustainability in Data Centers." In Computational Intelligence for Green Cloud Computing and Digital Waste Management. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1552-1.ch006.
Full textAbstract:
Data centers and transmission networks are crucial in the digital age, with the market expected to grow from $50 billion in 2021 to $120 billion by 2030. However, their extensive computing infrastructure and continuous operation generate significant heat, necessitating energy-intensive cooling systems. The IEA report revealed that data center power consumption surged by over 60% between 2015 and 2021, with transmission networks experiencing a 60% usage increase. Addressing these growing energy demands poses significant challenges for the industry, with some countries considering restrictions on new data center licenses due to environmental concerns. To mitigate the climate impact, the industry must prioritize the procurement of low-carbon or carbon-free electricity to reduce Scope 2 emissions related to electricity, heating, and cooling. Tech giants like AWS, Google, and Meta/Facebook have already adopted ambitious public targets, either running on carbon-free electricity or investing in global projects for cost-effective and large-scale emissions reduction.
4
Lakkarasu, Phanish. "Leveraging cloud-native microservices, containers, and serverless architectures for artificial intelligence pipelines." In Designing Scalable and Intelligent Cloud Architectures: An End-to-End Guide to AI Driven Platforms, MLOps Pipelines, and Data Engineering for Digital Transformation. Deep Science Publishing, 2025. https://doi.org/10.70593/978-93-49910-08-9_11.
Full textAbstract:
AI pipeline automation can be described as orchestrating the machine learning or deep learning life cycle with a software suite. These software suites could be open-source popular tools such as Kube Flow or MLflow, commercial services by platform providers such as AWS SageMaker and Azure ML, or something between which is a solution built on top of orchestration tools such as Apache Airflow or Google Cloud Workflows. These AI pipelines can provide benefits such as organized code, flexible workflows, and easy reproducibility. In machine learning, being able to reproduce your work (and someone doing the same) is crucial in both debugging and research. AI pipelines could also refer to the API for specific cloud-based AI services such as Google Cloud Vision or Azure's various Cognitive Services. The more granular the services being offered, the easier the life for most data scientists and engineers. Tasks such as image classification, object detection, image segmentation, optical character recognition, natural language processing, and so on, can be achieved with one API call and done in seconds. Often, the actual solution is calling the specific service and using a small amount of data to build specialized models. These specialized models can then be leveraged in conjunction with the data strategies used in traditional machine learning to achieve better quality predictions (Lakshman & Malik, 2010; Bernstein, 2014; Breck et al., 2017). All models referred to in this talk are stored in git repositories. Machine learning projects require team members to collaborate and be able to refer back to models used to generate high-quality predictions. You need the project type controls of using code without the bottlenecks of coding. These clear project goals and organization are model management systems. MLOps and other associated processes is the answer to reducing the timeline between idea and implementation, creating annotated models, and having the ability to reproduce.In recent years there have been revolutionary advances in cloud technology, allowing parties to create portable, dynamic, lightweight, and optimized applications without the headaches of typical deployment and operations. APaaS leverages cloud-native microservices, containers, and serverless architectures to enable agile and industrial strength enterprise-grade AI/ML pipelines for intelligent applications. Business units can supplement the core AI/ML competencies of the central teams by enabling these self-service enterprise pipelines, eliminating most of the enterprise pain points while allowing central teams to continue to dedicate their focus on innovation and expert models (Zaharia et al., 2010; Sato & Takahashi, 2020).
5
Rane, Nitin Liladhar, Suraj Kumar Mallick, Ömer Kaya, and Jayesh Rane. "Tools and frameworks for machine learning and deep learning: A review." In Applied Machine Learning and Deep Learning: Architectures and Techniques. Deep Science Publishing, 2024. http://dx.doi.org/10.70593/978-81-981271-4-3_4.
Full textAbstract:
The fast progress of Artificial Intelligence (AI) has resulted in major advancements in tools and structures for machine learning (ML) and deep learning (DL), changing numerous industries. This study offers a thorough examination of the most recent tools and frameworks that aid in the creation, implementation, and expansion of ML and DL models. The capabilities of key frameworks like TensorFlow, PyTorch, and Keras are assessed in their ability to facilitate the construction of complex neural networks by researchers and practitioners. Furthermore, cutting-edge resources such as Hugging Face Transformers for simplifying natural language processing duties and NVIDIA's RAPIDS suite for speeding up data science processes are praised for their inventive impacts. The research delves into incorporating these tools with cloud services such as Google Cloud AI, Amazon SageMaker, and Microsoft Azure ML, highlighting how they help make high-performance computing resources more accessible. The discussion revolves around how automated machine learning (AutoML) frameworks like Google's AutoML and H2O.ai ease the process of model selection and hyperparameter tuning, making it more accessible to non-experts. In addition, the study explores the significance of MLOps tools such as MLflow and Kubeflow, which support the continuous integration and deployment of ML workflows, guaranteeing scalability and reproducibility. This research offers valuable insights into the current ML and DL frameworks landscape by examining the strengths and limitations of these tools, helping researchers and industry professionals choose the right tools for their AI projects.
6
Vassilev, Vassil, Viktor Sowinski-Mydlarz, Pawel Gasiorowski, et al. "Building a Big Data Platform Using Software without Licence Costs." In Open-Source Horizons - Challenges and Opportunities for Collaboration and Innovation [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.1003268.
Full textAbstract:
This chapter presents the experience in developing and utilizing Big Data platforms using software without license costs, acquired while working on several projects at two research institutions – the Cyber Security Research Centre of London Metropolitan University in the United Kingdom and the GATE Institute of Sofia University in Bulgaria. Unlike the universal computational infrastructures available from large cloud service providers such as Amazon, Google, Microsoft and others, which provide only a wide range of universal tools, we implemented a more specialized solution for Big Data processing on a private cloud, tailored to the needs of academic institutions, public organizations and smaller enterprises which cannot afford high running costs, or do significant in-house development. Since most of the currently available commercial platforms for Big Data are based on open-source software, such a solution is fully compatible with enterprise solutions from leading vendors like Cloudera, HP, IBM, Oracle and others. Although such an approach may be considered less reliable due to the limited support, it also has many advantages, making it attractive for small institutions with limited budgets, research institutions working on innovative solutions and software houses developing new platforms and applications. It can be implemented entirely on the premises, avoiding cloud service costs and can be tailored to meet the specific needs of the organizations. At the same time, it retains the opportunity for scaling up and migrating the developed solutions as the situations evolve.
7
Palacios Barrera, Elvia. "Aplicación de la metodología Design Thinking y el recurso digital Jamboard." In Avances de las mujeres en las ciencias, las humanidades y todas las disciplinas. Libro científico I 2023. Universidad Autónoma Metropolitana. Unidad Azcapotzalco., 2023. http://dx.doi.org/10.24275/uama.128.10425.
Full textAbstract:
The year 2020 is marked by one of the most serious health scenarios in recent times. As a result of the pandemic caused by COVID-19, educational institutions at all levels had to take measures and implement strategies to carry out the teaching-learning processes that were usually carried out in person. The Metropolitan Autonomous University implemented the Emergent Remote Teaching Program (PEER), starting in the 20-I quarter. The PEER was designed as a project to support both students and teachers, providing a series of scaffolds to better develop university work. Starting from this context, it was necessary to search for and implement digital platforms, as well as methodologies that could coincide and help in the teaching process. In the Design careers, it is intended to promote and stimulate creative thinking through various didactic strategies that favor it, creating suitable environments for the teacher, with a constant presence in the process, to contribute to its development. A methodology that not only strengthens the development of innovative processes, but also collaborative work, is Design Thinking, since students participate in solving complex problems collectively, they learn to evaluate in a group; in addition to respecting the different ways of thinking to reach a final solution.. On the other hand, Google’s Jamboard digital whiteboard, called the smart screen, allows collaborative work, automatically saving the contributions of each participant in the cloud. Designing synchronous didactic strategies that combine the use of methodologies that in turn use smart screens then becomes a great challenge. This document shows the results obtained by applying the Design Thinking methodology, using the Jamboard digital whiteboard, to solve a design problem.
Conference papers on the topic "Google cloud projects"
1
Logofatu, Bogdan, Michaela Logofatu, Andreea maria Visan, and Dan dumitru Burdescu. "TEACHING AND LEARNING WITH CLOUD TECHNOLOGIES - DDLUB BEST PRACTICES." In eLSE 2017. Carol I National Defence University Publishing House, 2017. http://dx.doi.org/10.12753/2066-026x-17-065.
Full textAbstract:
The authors of this article have major contributions to the implementation and the efficient use of the cloud technologies within DDLUB. DDLUB is an acronym associated with the Distance Learning Department, founded in 1999 through the University of Bucharest Senate's Decision. During the last 18 years, the DDLUB was focused on: (1) DL (Distance Learning) study programs (design, accreditation, implementation, development); (2) the efficient use of digital technologies in education. That is why the central theme of the eLSE'2017 conference ("Could technology support learning efficiency?") is very exciting for the authors! In brief, the key points are to communicate and to collaborate with students and teachers through electronic platforms, in the cloud. This article is organised into 4 chapters. Chapter 1 will give a general review of cloud computing concept and will present the digital technology services usually provided in the “cloud”. Chapter 2 will include a number of initiatives and projects promoted by the authors; those projects demonstrate interest and concern of the authors in promoting digital technologies in education (continuously, during the last 25 years!). Chapter 3 - Starting with 2010, the authors developed projects including cloud computing /technologies especially those related to Google and other electronic platforms. The authors develop their activities in the academic institutions where the communication and collaboration between students and teachers are crucial; the authors have used efficiently cloud technologies like Gmail, Google Classroom, Google Drive etc. Chapter 4 includes authors' conclusions and recommendations for decision makers in the domain of education.
2
Tan, Xin, and Yongbeom Kim. "Cloud Computing for Education: A Case of Using Google Docs in MBA Group Projects." In 2011 International Conference on Business Computing and Global Informatization (BCGIn). IEEE, 2011. http://dx.doi.org/10.1109/bcgin.2011.169.
Full text
3
Du Bernard, Xavier, Jonathan Gallon, and Jérôme Massot. "The Gaia Explorer, a Powerful Search Platform." In Abu Dhabi International Petroleum Exhibition & Conference. SPE, 2021. http://dx.doi.org/10.2118/207837-ms.
Full textAbstract:
Abstract After two years of development, the GAIA Explorer is now ready to assist Geoscientists at Total! This knowledge platform works like a little Google, but with a focus solely on Geosciences - for the time being. The main goal of the GAIA Explorer is to save time finding the right information. Therefore, it is particularly useful for datarooms or after business acquisitions to quickly digest the knowledge, but also for feeding databases, exploration syntheses, reservoir studies, or even staff onboarding specially when remote working. With this additional time, Geoscientists can focus on tasks with added value, such as to synthesize, find analogies or propose alternative scenarios. This new companion automatically organizes and extracts knowledge from a large number of unstructured technical documents by using Machine Learning (ML). All the models relie on Google Cloud Platform (GCP) and have been trained on our own datasets, which cover main petroleum domains such as geosciences and operations. First, the layout of more than 75,000 document pages were analyzed for training a segmentation model, which extracts three types of content (text, images and tables). Secondly, the text content extracted from about 6,500 documents labelled amongst 30 classes was used to train a model for document classification. Thirdly, more than 55,000 images were categorized amongst 45 classes to customize a model of image classification covering a large panel of figures such as maps, logs, seismic sections, or core pictures. Finally, all the terms (n-grams) extracted from objects are compared with an inhouse thesaurus to automatically tag related topics such as basin, field, geological formation, acquisition, measure. All these elementary bricks are connected and used for feeding a knowledge database that can be quickly and exhaustively searched. Today, the GAIA Explorer searches within texts, images and tables from a corpus (document collection), which can be made up of both technical and operational reports, meeting presentations and academic publications. By combining queries (keywords or natural language) with a large array of filters (by classes and topics), the outcomes are easily refined and exploitable. Since the release of a production version in February 2021 at Total, about 180 users for 30 projects regularly use the tool for exploration and development purposes. This first version is following a continuous training cycle including active learning and, preliminary user feedback is good and admits that some information would have been difficult to locate without the GAIA Explorer. In the future, the GAIA Explorer could be significantly improved by implementing knowledge graph based on an ontology dedicated specific to petroleum domains. Along with the help of Specialists in related activities such as drilling, project or contract, the tool could cover the complete range of upstream topics and be useful for other business with time.
4
Silva Mota, Jhemeson, Marcio Vinicius Okimoto, Edna Dias Canedo, and Jhonatan Silva Mota. "Google Summer of Code Gender Diversity: An analysis of the last 4 editions." In Computer on the Beach. Universidade do Vale do Itajaí, 2021. http://dx.doi.org/10.14210/cotb.v12.p117-124.
Full textAbstract:
This work presents a comprehensive research about the participationof men and women in the area of Information and CommunicationsTechnology (ICT) through data extracted from the last foureditions of Google Summer of Code (GSoC). The goal of this workis to find Association Rules between gender characteristics andcoding using the Apriori Algorithm. A total of 61 association ruleswere generated through the aforementioned algorithm, being 22 ofthem found only in the data set with the women, 24 found only withthe men, and 15 applicable to both sets. We can cite as one of themain findings of this work the fact that the representativeness ofwomen in GSoC is decreasing in the last few years. Despite this, therepresentativeness of women in GSoC is above average, accordingto what has been reported in other studies in the literature in whichwomen are underrepresented. When it comes to the most utilizedtechnologies, we have “Python", “Java", “C++", “C" and “JavaScript"in the top. Analyzing technologies, it’s possible to realize that themain utilized technologies for men and women are similar, but, ingeneral, men are more likely linked to programming languages.The most common project topics are: “Event Management", “Web",“Web Development", “Data Science" and “Cloud" in the top. Thiscan represent how diverse the project topics of the database are,but not necessarily has something related to gender.
5
Azlan, Mohammad Azerul, Abd Kadir Mahamad, and Sharifah Saon. "Face Recognition of Passenger for Bus Services." In Conference on Faculty Electrical and Electronic Engineering 2021/2. UTHM, 2021. http://dx.doi.org/10.30880/eeee.2021.02.01.007.
Full textAbstract:
Most university students are using the bus provided by the university's management to move from one place to another place. The analysis are required to improvise the quality of the of bus services such as the amount of passenger that using the bus and information of passengers such as gender. The objectives of this project are to develop face recognition system based on gender using Raspberry Pi 4 and Intel Neural Compute Stick 2 and to test and validate the performance of the developed system for face classification and passenger counting system. Also this system is able to store passenger information into Google Firebase Cloud with Internet of Things. This system is used Raspbian in Raspberry Pi 4 with the libraries that used for face classification and recognition such as OpenCV and OpenVINO. This project able to detect faces of the passengers soon as they ride the bus and determine gender of the passengers and count passengers according gender and the information of the passengers will stored in Google Firebase. There are some recommendation that need to be added in this project to improve efficiency of the system.
6
Logofatu, Bogdan, and Andreea maria Visan. "NEW TRENDS IN THE EDUCATIONAL AREA. CASE STUDY REGARDING THE USABILITY OF GOOGLE APPS TOOLS WITHIN THE DEPARTMENT FOR DISTANCE LEARNING." In eLSE 2015. Carol I National Defence University Publishing House, 2015. http://dx.doi.org/10.12753/2066-026x-15-171.
Full textAbstract:
At Stanford Commencement Address in 2005, Steve Jobs said : "You can't connect the dots looking forward you can only connect them looking backwards. So you have to trust that the dots will somehow connect in your future.". In the 21st century everything is moving faster, an innovation is "new" only for a short period of time. This changes are affecting all areas, including education. But what if education develops its "future" by founding itself in the "past" again? Could the "yesterday" in which the Romanian education system has remained be the source of inspiration for the "tomorrow" that the present development promises? This paper is aiming to present the valuable journey that the Department for Distance Learning team has traveled since the starting point, in may 1999 and until now, a journey that hasn't yet reached the end. Over the years many valuable project have been developed and implemented by CREDIS team (CREDIS is the acronym for the Department for Distance Learning): new higher education study programs (BA and MA) especially designed for pre-service and in-service school teachers; a new BA study program (240 ECTS) focusing on "Information Technology"; new LLL programs (CISCO Networking Academy, Microsoft IT Academy, ECDL), with the main purpose to find inspiration, answers and proposals for "tomorrow" education. The main interests of this paper are centered on the last years in which CREDIS team has focussed on identifying, developing and promoting e-learning solutions in Romania. In this regard, one of the most important challenges was and is the cloud technology that has quickly replaced the traditional learning management system used in universities. Universities from all over the world are now using the cloud computing technology to improve collaboration between teachers and students, to increase the efficiency of their classroom but also to save costs without sacrificing privacy or security. Aligning with the directions recommended by the European Union on modernisation of higher education CREDIS has successfully implemented Google Apps tools within their students. Google Apps For Education (acronym GAFE) is a free cloud-based messaging and collaboration platform that includes mail, calendaring, IM, as well as Web-based collaborative documents, spreadsheets, presentations, and sites. Students opinion regarding the opportunities provided by this applications on communication, file storage, collaborative documents, spreadsheets and presentation in a "cloud" campus will be presented in the paper.
7
Logofatu, Bogdan, Andreea maria Visan, and Camelia Ungureanu. "GOOGLE CLASSROOM - THE NEW EDUCATIONAL CHALLENGE. PILOT TEST WITHIN THE DEPARTMENT FOR DISTANCE LEARNING." In eLSE 2015. Carol I National Defence University Publishing House, 2015. http://dx.doi.org/10.12753/2066-026x-15-166.
Full textAbstract:
The landscape of the educational area has witnessed a continuous transformation in the past years due to new technologies. Higher educational institutes from all over the world are competing in all kinds of learning experience with the main objective of finding the best practices for their students. The main purpose of all this experiences is to enrich learning through a new generation of pedagogical methods. One of the highest demands for Romanian educational system is the transition between the traditional classroom, which is limited at one teacher and his students in one room at the same time, with limited learning resources, to an open learning process in which teachers and students are equals contributors and in which learning resources are always shaping. This paper interests are centered on emerging educational technology through effective learning experiences in a cloud computing environment. Our main interest is to present a new way of doing things on campus by using Google classroom (first pilot test in Romania) within the Department for Distance Learning. Over the years the Department for Distance Learning had students of different ages, from 18+ to 50+, students from all over the country and abroad and they all had the same purpose: to learn. By working with different people, with different ways of learning and also different attitudes, opinions, ideas, needs and stock of knowledge, our mission was and still is to provide opportunities to learn for everyone, to customize learning. And what better way of doing it if not by implementing the latest trends in teaching? To achieve this mission our team started to use Google classroom, which is a great distance assessment tool that allows crowd participation and peer support. It is also promoting self assessment and reflection, it offers great help in managing class resources and a good guideline for e-feedback. It allows students and teachers to work quickly from anywhere and it uses the same web based tools that students and teachers among are using it at home. The challenges for the Department for Distance Learning team that came with the launch of this new project test are multiple but there are also a lot of opportunities created for teaching and learning as it will be presented in the paper.
8
Zhu, Alex, and Carlos Gonzalez. "A Real-Time System to Bridge Communication Gaps Between Deaf and Hearing Communities using Machine Learning and Computer Vision." In 9th International Conference on Artificial Intelligence, Soft Computing And Applications. Academy & Industry Research Collaboration, 2025. https://doi.org/10.5121/csit.2025.150411.
Full textAbstract:
Communication barriers between the deaf and hearing communities remain a significant challenge due to the lack of widespread knowledge of American Sign Language (ASL) [1]. Motivated by a personal experience at a Boy Scouts summer camp, I developed a real-time ASL translating app to bridge this gap [2]. The app leverages Google’s MediaPipe for precise hand landmark detection and the PointNet model for gesture recognition, translating ASL letters into text in real time [3]. Built with Flutter and Dart for a seamless cross-platform experience, the app integrates a Flask-based backend for efficient processing. Key challenges, including environmental variability and achieving computational efficiency, were addressed through data augmentation, model optimization, and extensive testing. The experimentation demonstrated high accuracy and usability, validating the app’s effectiveness across diverse real-world scenarios. Future plans include expanding capabilities to recognize full ASL sentences, integrating text-to-speech functionality, and leveraging cloud storage for scalability. This project exemplifies how technology can foster inclusivity, creating a practical tool to empower communication and bridge societal gaps.
9
Vasilescu, Cristian, Cristian Beceanu, and Ijaz Hussain. "COLLABORATIVE OBJECT RECOGNITION FOR PARKING MANAGEMENT." In eLSE 2019. Carol I National Defence University Publishing House, 2019. http://dx.doi.org/10.12753/2066-026x-19-026.
Full textAbstract:
The number of vehicles is constantly increasing and one of the challenges is efficient parking management. For this case, object recognition is required to detect and recognize cars, pedestrians, bicycles and obstacles in an indoor car parking. Cars must be recognized in order to indicate to drivers where to park their car, providing a semantic web of things. Vehicle recognition including car registration number must be followed by tracking, i.e., tracking the car in all subsequent image frames even if the image is partially obturated. Pedestrians should also be recognized for avoiding accidents, as well as being aware of different static objects/obstacles. This paper deals with object recognition through the Deep Learning (DL) method with Convolutional Neural Network (CNN), this being the newest and most efficient method based on our results in project VIRTUOSE (Virtualized Video Services). The paper briefly introduces CNN, image databases, and performance metrics. Then, several collaborative web methods/programs for object recognition are analyzed and compared, namely YOLO (You only look once), Google Cloud Vision, Advanced Convolutional Neural Networks in the Tensorflow framework and OpenCV. We analyze how programs detect and recognize multiple types of objects in an image or video, in real time. In case of video analysis on a PC / laptop, it is necessary that it has an NVIDIA module with several GPUs. The comparison refers especially to the precision of recognition, image resolution, the computation time (real-time response), viewpoint variations. The novelty of the work is the special structure of CNN suitable to the parking management application.
10
Rußkamp, Nina, Claas Digmayer, Eva-Maria Jakobs, Florens Burgert, Marisa Schirmer, and Susanne Mütze Niewöhner. "New ways to design next-generation CAM systems. An integrated approach of co-creation and process modeling." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1002682.
Full textAbstract:
Computer-Aided Manufacturing systems are common means to increase the flexibility and efficiency of production planning in manufacturing companies. Digital transformation processes increase innovation cycles and product individualization [1] and, by doing so, the complexity of production planning and CAM systems use [2]. In the R&D project CAM2030 a new generation of CAM systems is developed by integrating innovative technologies (AI, cloud computing, evolutionary algorithms). The highly innovative process requires new methods. This paper presents an integrated methodological approach that enriches co-creation methods [3] by integrating visualization methods of process modeling [4]. The methodology was developed in three steps: concept development, concept realization, and concept evaluation. Concept development: Due to the Covid-19 pandemic, a remote co-creation workshop was designed based on two assumptions: (1) Co-creation at an early stage of the innovation process benefits from integrating users’ perspective and need information [5]. (2) Modeling and visualizing CAM planning processes allows to build up a shared understanding of the status quo. Human-centered work design experts compiled, modeled, and visualized the project-specific CAM planning process with the C3 modeling method [4]. Technical communication experts focused on methods and tools to gather need information (requirements for intelligent CAM systems) remotely. The workshop comprises three parts: warm-up challenge to identify no-go design features of CAM systems, discussion of the CAM planning process and model, and derivation of design requirements and automation potential. Each part uses different practices, e.g., teams working in separate breakout sessions, documenting their results in a shared document on Google Docs in real-time. Concept realization: The workshop was conducted in February 2021 via Zoom. The participants (CAM users, software developers, researchers) (n=21) were acquired within the project consortium. The workshop was audio- and video-recorded. The participants’ notes were stored in Google Docs. The transcribed audio data were enriched with additional information, e.g., participants’ notes. After the workshop, the data were used to integrate, categorize, and prioritize need information and to revise the process model. Concept evaluation: The concept was evaluated by the workshop participants and the workshop leader team guided by two research questions: Is this methodological approach suitable for innovation processes? What are the potentials and challenges of the approach? The approach proved to be highly productive. The integration of co-creation and process modeling seems to be a promising approach to involve diverse perspectives in the design of intelligent CAM systems. The process model supported the workshop participants in creating a shared understanding of the CAM planning process and identifying potentials for optimization and automation. The collaboration in heterogeneous groups yielded a structured catalog of requirements that will go into the further innovation process of CAM systems. Shortcomings concern the live adaptation of the process model as well as bringing together partial results from different groups and cluster ideas. Under pandemic conditions, the approach is practical to a limited extent. Future research will focus on how the nexus of co-creation and process modeling can be advanced to enrich the design of innovative software systems.
Reports on the topic "Google cloud projects"
1
Osadcha, Kateryna P., and Viacheslav V. Osadchyi. The use of cloud computing technology in professional training of future programmers. [б. в.], 2021. http://dx.doi.org/10.31812/123456789/4435.
Full textAbstract:
The article provides a brief analysis of the current state of the study of cloud technologies by future software engineers at foreign and Ukrainian universities. The author experience in the application of cloud technologies in the training of future software engineers in Ukraine is presented. The application of cloud business automation systems, online services to monitor the implementation of the software projects, Google services for collaboration, planning and productivity while studying professional disciplines and carrying out diploma projects is described. Based on the survey conducted at Stackoverflow, the state of application of cloud technologies by software engineers around the world has been analyzed. The cloud technologies that are not studied at the analyzed universities of Ukraine and those that are not popular with software developers in the world, but studied at Ukrainian universities by future software engineers are outlined. Conclusions are made on the modernization of training programs for future software engineers. Topics for the study of cloud technologies by future software engineers in the content of professional disciplines are proposed.
2
Korobeinikova, Tetiana I., Nataliia P. Volkova, Svitlana P. Kozhushko, et al. Google cloud services as a way to enhance learning and teaching at university. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3854.
Full textAbstract:
The article is devoted to the issue of a cloud-based learning system implementation as a powerful strategy for future specialists’ training at higher educational establishments. Using cloud computing in self-work management of the university courses is essential to equip students with a workload of appropriate educational materials and variable activities for professional training. Theoretical and empirical research methods were applied to select the appropriate services and tools for organizing students’ self-work at university. Critical analysis of scientific literature, synthesis of the data, didactic observation of the educational process, designing of the skeleton for university courses, questionnaires enabled to facilitate the study of the issue. G Suite has been chosen to enhance the quality of training of prospective specialists at a higher educational establishment. This paper introduces the outcomes of the project on applying Google Classroom in the management of students’ self-work while studying university courses. The focus of the first stage of the project was on testing pilot versions of the courses with the aim to work out the requirements and recommendations for incorporation general blended learning model of university courses. Particular attention is drawn to the designed model of the university course based on the curriculum with the necessary components of blended learning in the G Suite virtual environment. Cloud-based higher education is considered as a prospective tool for design of university courses with the need for further research and implementation.