Case Study
Optimizing Cloud & AI Strategy for CTE Program Provider
Key Result Highlights
- Successfully assessed and addressed the client's cloud expansion needs.
- Integrated a versatile code editor supporting programming languages like Python and Node.js.
- Executed cloud expansion strategies, incorporating support from LLM models for efficient code execution.
- Crafted comprehensive requirements for an AI-based strategy.
The Client
The client is a well-known CTE program provider seeking innovative
solutions to enhance their cloud infrastructure and AI capabilities.
The Challenge
The client was looking for a strategic partner to navigate their cloud
expansion needs, formulate AI-based strategy requirements, and enhance
their cloud infrastructure.
Critical Success Parameters
- Comprehensive assessment of cloud expansion needs.
- Crafting requirements for an AI-based strategy.
- Augmentation of the existing cloud infrastructure.
- Seamless integration of a versatile code editor supporting Python and Node.js.
- Effective support for programming languages and efficient code execution.
Our Approach
- Leveraged GCP-based cloud infrastructure and Vertex AI solution for a robust foundation, ensuring scalability and advanced AI capabilities.
- Employed containerized architectures like GKE to optimize costs.
- Initiated GCP cloud setup using the Terraform tool, expediting the deployment process.
- Configured the platform with GKE, Cloud SQL, and Cloud VPN for the client’s asset management application, ensuring a tailored and optimized cloud environment.
- Developed a frontend UI in ReactJS and created a Code Editor backend API in NodeJS, following a microservice architecture, enhancing modularity and flexibility.
- Containerized the solution using Docker for deployment in GKE, facilitating seamless deployment and scalability.
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