Working closely with Sweeeft.ai’s engineers, DoiT conducted a meticulous assessment of the existing architecture, identifying improvement and potential bottlenecks. Based on this analysis, DoiT recommended a cloud-native approach using AWS services.

To ensure seamless integration, DoiT collaborated with Sweeeft.ai’s team to determine the optimal combinations of Python, PyTorch, and HuggingFace versions compatible with AWS Deep Learning Containers. Through targeted training sessions, DoiT transferred this knowledge to empower Sweeeft.ai’s team to independently manage and maintain its machine learning pipeline.

DoiT guided best practices for organizing the machine learning lifecycle, including development, storage, model deployment, and CI/CD automation. Sweeeft.ai implemented a robust and scalable infrastructure for its AI initiatives by leveraging the ML Lens of the AWS Well-Architected framework.

Cost optimization was a key focus for DoiT. By enabling auto-scaling policies on SageMaker asynchronous inference endpoints, DoiT helped Sweeeft.ai eliminate unnecessary resource consumption during idle periods while ensuring rapid scalability to meet spikes in demand.

Throughout the project, DoiT prioritized knowledge transfer and empowered Sweeeft.ai’s team to own their AWS AI infrastructure. By doing so, Sweeeft.ai gained the capability to operate and optimize its AI systems independently.

The following diagram illustrates the architecture implemented by Sweeeft.ai for its resume categorization project, which effectively balances performance and cost considerations:

By partnering with DoiT International, Sweeeft.ai successfully modernized its AI infrastructure, leveraging the power of AWS services and adopting industry best practices. The collaboration with DoiT has empowered Sweeeft.ai to boost its productivity, streamline operations, and achieve its business objectives in the field of HR.