From Manual to Managed: Platform Engineering for Scalable AI

This case study explores how a leading technology company transformed its AI capabilities by implementing a robust platform engineering approach. By addressing the challenges of manually deployed infrastructure and a lack of platform engineering expertise, the company achieved improved efficiency, scalability, and innovation.

The company, a pioneer in its industry, faced a growing need for a more scalable and accessible AI platform. Their existing infrastructure was manually deployed, limiting access and repeatability. Additionally, the company lacked the necessary expertise to build platforms that could handle production-level workloads.

The Challenge

• Manual deployment: Manual infrastructure provisioning was time-consuming, error-prone, and limited accessibility.
• Lack of repeatability: Manual processes made it difficult to consistently deploy and manage infrastructure.
• Scalability limitations: The existing infrastructure struggled to handle increasing data volumes and AI workloads.
• Platform engineering expertise gap: The company lacked the skills to build and maintain a scalable and reliable platform for AI.

The Solution: Platform Engineering

The company implemented a platform engineering approach, focusing on building a centralised, scalable, and repeatable platform for AI development. Key components of the solution included:

• Infrastructure as code: Adopting infrastructure as code practices to automate provisioning, configuration, and management of infrastructure resources.
• Self-service platform: Creating a self-service platform that empowered data scientists to provision and manage their own environments.
• Scalability and performance: Designing the platform to handle increasing data volumes and AI workloads, ensuring high performance and availability.
• Platform engineering team: Establishing a dedicated platform engineering team to oversee the development, maintenance, and optimization of the platform.

Results

• Increased efficiency: Automated infrastructure provisioning and management reduced manual tasks and empowered the AI teams.
• Improved accessibility: The self-service platform provided the AI developers with greater autonomy and access to resources.
• Enhanced scalability: The platform's scalable architecture enabled the company to handle growing data volumes and AI workloads.
• Accelerated AI innovation: The platform provided a solid foundation for the AI teams to experiment and develop new applications.

Conclusion

By embracing platform engineering, the company successfully overcame the challenges of manual infrastructure deployment and a lack of platform engineering expertise. The new platform enabled them to scale their AI capabilities, improve efficiency, and accelerate innovation. This case study demonstrates the critical role of platform engineering in unlocking the full potential of AI within organisations.