We specialize in end-to-end application development and cloud technology integration while reducing costs. Creating automated processes saves valuable time for employees and the company
Machine Learning: Building intelligent systems for data analysis and providing personalized recommendations.
Natural Language Processing: Creating advanced chatbots, automatic translation, and sentiment analysis.
Computer Vision: Developing systems for image recognition, image processing, and overlaying images on faces or structures.
Solutions for CRM and ERP systems.
Secure Digital Signature:
Digital document signing.
Electronic identity verification.
We ensure the preparation of a generic infrastructure that easily integrates with additional products, striving for zero errors and providing reliable and efficient solutions.
Our company specializes in providing website development services, landing pages, and promotional websites, tailored to the specific needs of each client. We guide the client from the planning stage to the launch, ensuring modern design, optimal user experience, and the use of advanced technologies.
is a critical aspect of the company as it does not want to waste unnecessary resources while it can create automated processes such as version updates, environment management, database migration, and more. Without careful attention, valuable time and important data may be lost.
After creating DevOps processes and automation, it is necessary to manage and maintain the system. This is not always straightforward, especially in this field. Therefore, it is important to maintain the existing system without downtime and simultaneously add changes and features to it.
There are cases where we have an existing system, but we want to add a feature like language transcription, image processing, or a bot. In this case, integration with a model developed by a data scientist is needed. We aim to reduce costs by using expensive resources like graphics processors and powerful machines. This can be achieved by paying only for the usage rather than the entire machine's cloud existence.
Not every company is suited to set up and maintain a single cloud server. Therefore, we consider using various tools provided by Google Cloud, which avoids reinventing the wheel. Using these tools can save resources and valuable time, such as domain connections, load balancing, database management, clustering, easy and efficient version updates, and many other convenient tools.
Similar to Google Cloud, Amazon specializes in various fields, one of which is artificial intelligence, such as image processing and integrating systems that require powerful graphics processors.
There is an option to develop a product and deploy it to the cloud as is, with client access points. However, this can be cumbersome and time-consuming. Therefore, in some cases, it is preferable to package the server in Docker and Kubernetes to manage loads dynamically and automatically, easily and efficiently.
I would like to share a FastGAN MLOps project that I completed for a satisfied customer.
We recently embarked on a project to integrate a FastGAN model into a GPU virtual machine (VM) environment in AWS, with the goal of optimizing cost-efficiency while maintaining high-performance capabilities. The primary objective was to deploy the model on-demand and provide access through a REST API endpoint.
Project Highlights
Seamless Integration: The process of assimilating the FastGAN model into the GPU VM was executed smoothly. The GPU's computational power significantly enhanced the model's performance, allowing for faster image generation and processing
Cost Efficiency: By utilizing an on-demand model deployment approach, we effectively managed and reduced costs. The GPU VM was provisioned only when needed, eliminating unnecessary expenses associated with continuous running instances
REST API Endpoint: The implementation of a REST API endpoint proved to be a robust solution for accessing the FastGAN model. It facilitated easy and flexible interactions with the model, enabling seamless integration with existing systems and applications
Performance and Reliability: The FastGAN model demonstrated high performance with rapid processing times. The system's reliability ensured consistent availability and responsiveness, efficiently meeting our operational demands
User Experience: The deployment process was user-friendly, and the API provided clear and comprehensive documentation. This made it straightforward for our development team to integrate and utilize the FastGAN model within various projects
Overall, this MLOps project successfully met the customer's objectives of cost reduction, performance enhancement, and operational flexibility. The combination of GPU-based deployment, on-demand model activation, and REST API access has proven to be a highly effective solution for the customer's needs
From this project, I learned that the combination of Serverless technologies, such as AWS Lambda, and native DevOps practices can effectively meet diverse needs
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Initial consultation and writing of a specification document are free of additional charge.
054-2197024
shimi@apisoul.com