Project Overview

Theia Scientific specializes in augmenting scientific and engineering workflows through AI, machine learning, and near-edge computing technologies. Their flagship product, Theiascope™, is an edge-computing device designed for real-time image analysis in scientific research. This platform, which is customizable and web-based, works with any microscope or image acquisition system to enhance efficiency and discovery in scientific research. To expand their offerings, Theia Scientific aimed to introduce a Software as a Service (SaaS) model for on-demand post-processing of data in the cloud. Avahi was engaged to design and develop this cloud-based solution, focusing on the preliminary stages of design and analysis to deliver a comprehensive fixed scope/fixed price proposal for the MVP.

Challenges

Theia Scientific faced several challenges in transitioning to a SaaS model:

• Integration: Seamlessly integrating cloud-based post-processing with existing edge-computing capabilities.
• Scalability: Ensuring the solution could scale to meet the demands of multiple
• simultaneous users.
• User Experience: Maintaining a user-friendly interface for both novice and experienced users.
• Security: Protecting sensitive scientific data in a cloud environment.
• Performance: Delivering real-time processing capabilities in a cloud-based solution.

Solution

Avahi proposed a comprehensive approach to address these challenges, focusing on the development of an MVP that aligns with Theia Scientific’s strategic business goals. The engagement aimed to design a solution architecture that leverages AWS services, ensuring scalability, security, and performance.

Key Deliverables

• MVP Hypothesis: A concise statement reflecting the core assumption the product’s success hinges upon.
• User Stories and Acceptance Criteria: Detailed specifications outlining the functionality and expected performance of the MVP.
• Wireframes: Visual representations of the MVP’s interface to assist in visualizing its structure and layout.
• Solution Architecture: A design for deploying the solution to AWS, adhering to AWS’s Well-Architected principles.

Solution Architecture

The solution architecture leveraged various AWS services to implement the deliverables and ensure a robust, scalable platform.

Data Acquisition and Storage

• Amazon S3: Used for storing input data, scientific images, and post-processed results. S3’s scalability and durability ensured efficient handling of large volumes of data.
• Amazon RDS: Provided a scalable and managed database service for storing user data, metadata, and processing results.

Event-Driven Processing

• AWS Lambda: Deployed to run event-driven functions that process data and manage various stages of the pipeline. Lambda functions were used to trigger data processing workflows and handle real-time data updates.

Machine Learning and AI

• AWS SageMaker: Utilized for developing, training, and deploying machine learning models that enhance image analysis and post-processing capabilities.
• AWS Bedrock: Employed for scalable managed Generative AI services, ensuring efficient AI model deployment.

API Management

• Amazon API Gateway: Provided a managed service to create, publish, maintain, and secure APIs. This facilitated seamless communication between the front-end applications and backend services

Monitoring and Logging

• AWS CloudWatch: Implemented for comprehensive monitoring and logging. CloudWatch provided real-time insights into system performance, user interactions, and the health of the deployed models.

Project Impact

Enhanced Customer Experience – The SaaS solution significantly improved the customer experience by providing on-demand, cloud-based post-processing of scientific data. This allowed researchers to perform advanced analyses without the need for extensive local computing resources, leading to increased efficiency and productivity.

Operational Efficiency – By automating data processing workflows and leveraging scalable cloud infrastructure, Theia Scientific saw a reduction in manual efforts and operational costs. The efficient data handling and machine learning models ensured timely and accurate processing of scientific images.

Scalability and Future-Proofing – The architecture designed by Avahi ensured that Theia Scientific’s platform was scalable and ready for future enhancements. The integration of AWS services provided a robust foundation for handling increased user loads and additional functionalities as the customer base grows.

Detailed Insights and Analytics – The deployment of AWS CloudWatch and other monitoring tools provided Theia Scientific with detailed insights into user behavior, model performance, and system health. These analytics were crucial for continuous improvement and optimization of the platform.

Conclusion

The collaboration between Theia Scientific and Avahi led to the successful design and development of a cloud-based SaaS solution for scientific data post-processing. Leveraging AWS services, Theia Scientific was able to enhance customer experience, improve operational efficiency, and build a scalable platform ready for future growth. Avahi’s expertise in AWS solutions played a pivotal role in this transformation, demonstrating the power of cloud computing and AI in advancing scientific research.