Uitware
visit site- $10,000+
- 10 - 49 employees
- L'viv, Ukraine
Client Insights
Industry Expertise
25%
15%
15%
10%
5%
5%
5%
5%
5%
Client Size Distribution
Small Business (<$10M) 60%
Midmarket ($10M - $1B) 40%
Common Project Size
$10K-$49K 3 projects
<$10K 2 projects
$50K-$199K 1 project
Clients
This provider has not added their key clients.
Highlights from Recent Projects
Uitware provided cloud consulting services to Univisia, a company that offers software development and business application configuration services. The project involved developing a comprehensive Azure migration strategy, delivering specialized Azure training sessions, and reviewing Univisia's AWS architecture. The Azure migration strategy included an assessment of Univisia's existing infrastructure, cost analysis, and a roadmap for the seamless migration of applications and services. The training sessions were designed to enhance Univisia's operational capabilities and facilitate smoother management of the Azure environment post-migration. The AWS architecture review evaluated Univisia's cloud infrastructure and provided recommendations for performance, security, and cost efficiency improvements.
For TeraCrunch, a company that builds AI and ML-focused solutions, Uitware was hired to migrate multiple applications with databases to PaaS from Virtual Machines in Azure and implement a structured deployment strategy. The scope of the project included configuring health checks, application logging, deployment and rollback strategy, staging and/or dev instance, and SFTP for file uploads. The project also required guidance on database hosting, automated CSV import and file retirement, and Python scripting to integrate with Azure services. The project aimed to ensure high availability, scalability, and reliability of the application while incorporating essential features for monitoring, deployment, and data management.
Uitware was hired by Happyusers, a company engaged in 3D development, to implement and design a ML Deployment workflow in Azure Cloud. The project involved deploying a machine learning application into Azure Cloud, covering architecture design, infrastructure setup, and deployment automation. Key deliverables included a scalable Azure environment, containerized ML models, and CI/CD pipelines for automated updates. Comprehensive testing and detailed documentation ensured the system's reliability and maintainability. Post-deployment support and knowledge transfer were also provided to enable smooth operations and future scalability.