Enhancing Scalability and Efficiency for an AI Research Platform • BigCircle Blog

BigCircle's team worked on this project, but we can not disclose the client's at this moment.

About The Client

A leader in bridging AI and academic research introduced an AI research assistant to streamline the process of writing research papers. Utilizing available digital research publications, the tool aids researchers in comprehending literature and drafting informed papers. Its integration of machine learning allows for personalized assistance based on user-uploaded content, adapting its recommendations to individual research interests.

Country

Industry

Timeline

2022-2023

Challenge

Faced with scalability and architectural limitations, the client encountered significant hurdles during their expansion effort. The monolithic system design impeded scale and responsiveness, while the AI indexing service’s lengthy processing time for research papers led to user experience degradation and increased operational costs. These challenges highlighted the urgent need for a technological revamp to enhance scalability, reduce processing times, and support future growth.

These obstacles highlighted the critical need for targeted improvements to the company's tech base. To unlock its full potential and transform academic research, the client needed a partner skilled in solving these core issues to enhance scalability, cut down on processing times, and support the platform's continued development and creativity.

Partnership Objective

To transform the AI research assistant into a scalable, high-performance platform capable of serving a global user base efficiently

Before

The client faced significant challenges in scaling their AI research assistant platform. The monolithic system architecturelimited scalability and responsiveness, leading to prolonged AI indexing serviceprocessing times. These issues not only degraded user experience but also increased operational costs, hindering the platform's growth and innovation potential.

After

The AI research assistant platform now features a microservices architecture, guaranteeing scalability and high performance. We've significantly cut down the processing time of the AI indexing service, leading to enhanced user satisfaction and better system efficiency. Additionally, adopting serverless technologies has greatly reduced operational costs, allowing the client to concentrate on future expansion and tech improvements.

3. Objectives

In launching this transformative project with the client, our team began with a well-defined vision and a set of specific objectives designed to tackle the significant challenges encountered. The foremost aim was not just to address the current problems but also to establish a robust foundation for the company's future development and progress. These objectives were formulated based on the principles of specificity, measurability, achievability, relevance, and timely execution (SMART criteria), ensuring a systematic method for achieving measurable results.

Transition to Microservices Architecture

Our main goal was to swap the existing monolithic framework for a modular, microservices-based architecture. We aimed to boost the system's scalability and efficiency, making it easier to handle growing user engagement and data volume. This change also sought to simplify maintenance and updates, quickening our ability to meet market needs and keep up with tech advancements.

Utilize serverless for efficiency

To further improve scalability and operational efficiency, our plan involved adopting serverless computing services to remove the need for managing servers. This approach enabled the client to focus on their main product features and innovation, allowing for automatic adjustment of resources to meet demand as it happens.

Optimize AI Indexing Service Workflow

A key goal was to drastically decrease the processing time for the AI indexing service, from its current duration of seven minutes per document to a shorter, more efficient interval. This enhancement was crucial for elevating the user experience, facilitating quicker feedback and interaction with the platform. Realizing this objective required the refinement of algorithms, the application of parallel processing techniques, and the investigation of cutting-edge data processing technologies.

Ensure System's Future-Proofing

In addition to tackling the immediate obstacles, a broad goal was to create an infrastructure that could support the client's long-term aspirations and expansion plans. This involved ensuring the infrastructure's adaptability for seamless integration of future innovations, scaling to meet the needs of a growing user community, and consistently delivering high performance and satisfaction.

Achieve Cost-Effective Operation

At the heart of the project's success was making sure the new system design and procedures were both practical and economical. This involved optimizing resource usage and costs associated with cloud services, processing power, and storage, ensuring that the platform's operational expenses are sustainable and aligned with business growth.

Solutions Implemented

After an intensive 18-month development cycle, our team successfully deployed a revamped AI research assistant platform. This project transformed the client's service, making it more scalable, efficient, and user-focused. It now serves as a cornerstone tool in the global academic community, facilitating research and insights with unprecedented efficiency.

The strategy for overhauling the client's technological foundation and tackling the present challenges was comprehensive and well-planned. Our team meticulously crafted and executed solutions that spanned infrastructure improvements, database management enhancements, and optimizations in AI service efficiency, aligning with the established goals. The deployment approach was guided by principles of agility, scalability, and readiness for future developments, guaranteeing that the client could overcome existing hurdles while remaining adaptable and competitive in the changing technology landscape.

4.1 Decoupling the Monolithic Architecture

Objective: Transition to a scalable, manageable microservices architecture.

Implementation Strategy:

Microservices Architecture Definition: We commenced by dissecting the existing monolithic system to delineate distinct service boundaries, such as User Management, Document Processing, and AI Model Training. This segmentation allowed for modular development and deployment.
Adoption of Serverless Technologies: AWS Lambda was adopted to run the microservices, eliminating the need for traditional server management and enabling on-demand scaling that matched the system's operational demands. This transition to serverless computing was pivotal in enhancing system scalability and cost-efficiency.
Continuous Integration and Continuous Deployment (CI/CD): We leveraged GitHub Actions to automate the deployment pipeline, facilitating smoother and more rapid updates as well as ensuring consistencies across environments through Infrastructure as Code (IaC) practices.

4.2 Scalable and Managed Database Solution

Objective: Enhance database scalability, performance, and maintainability through migration to Amazon RDS for PostgreSQL.

Implementation Strategy:

Database Migration to Amazon RDS: The transition involved migrating the existing database from its colocated environment with the legacy API server to a cloud-native Amazon RDS for PostgreSQL instance. This move aimed at achieving higher performance, automatic scaling, and improved data integrity.
Implementation of Read Replicas and Backup Strategies: To ensure high availability and load balancing for read operations, we deployed multiple read replicas. Automated backups and manual snapshot features of Amazon RDS were leveraged to safeguard data against loss and ensure business continuity.
Connection Pooling with AWS Lambda: We introduced connection pooling mechanisms to optimize database connections established by AWS Lambda, significantly reducing overheads related to database connections and enhancing overall system performance.

4.3 Improving the AI Indexing Service

Objective: Significantly reduce the AI indexing service's document processing time while maintaining system scalability and responsiveness.

Implementation Strategy:

Integration of GROBID with Amazon ECS: GROBID was deployed within Amazon Elastic Container Service (ECS) to handle the extraction and analysis of bibliographic information from academic papers. This containerized deployment facilitated scalable and efficient document processing.
Event-driven Architecture with Amazon S3 and SQS: We adopted an event-driven architecture where document uploads to Amazon S3 triggered event notifications. These notifications were then processed by Amazon SQS to manage the workload efficiently, ensuring dynamic scaling of processing resources based on demand.
Data Indexing with Amazon Elasticsearch Service: Following the processing with GROBID, the extracted data was indexed using a managed search service. This facilitated advanced search and analysis functions, crucial for the AI models to deliver pertinent suggestions and insights.
Autoscaling and Robust Error Handling: Autoscaling policies for the ECS service housing GROBID ensured that resource allocation matched the incoming workload, optimizing both performance and cost. A Dead Letter Queue (DLQ) strategy was implemented to manage processing failures, allowing for the isolation and reprocessing of problematic documents.

Impact on Client’s Business

Reduced AI Indexing Service Processing Time: The optimization of the AI indexing service, including the integration of GROBID and the adoption of an event-driven architecture, has led to a dramatic reduction in document processing time. From the initial seven minutes per paper, the processing time has been cut significantly, vastly improving the user experience and enabling researchers to receive timely assistance and insights for their academic work.
Enhanced System Scalability: The shift to a microservices architecture and the adoption of serverless computing technologies have significantly enhanced scalability for the platform. It is now equipped to seamlessly manage growing volumes of user interactions and data processing activities, maintaining high performance levels. This advancement supports the client's ambitious plans for expansion and development.
Cost Efficiency Improvements: By utilizing managed cloud services and enhancing resource efficiency, we've realized significant savings in operational expenses for the client. The embrace of serverless technologies and streamlined database management approaches has reduced the costs associated with scaling, ensuring the platform's economic feasibility and sustainability over time.
High Availability and Performance: The introduction of Amazon RDS read replicas and the implementation of connection pooling have ensured high availability and boosted the performance of the database services. This enhancement has further solidified the platform's reliability, providing a seamless research experience to users even under peak load conditions.

Technologies Used

Technology Integration

The adoption of an event-driven architecture necessitated a nuanced selection of technologies, each playing a crucial role in achieving the desired outcomes of scalability, performance, and user engagement.

Backend Processing: Node.js was chosen for its non-blocking I/O model, making it exceptionally well-suited for building scalable network applications that can efficiently process a high volume of events and requests.
Frontend Development: React was utilized for creating dynamic and responsive user interfaces, enhanced by Next.js for its server-side rendering capabilities. This combination ensures quick loading times and a seamless interactive experience for users navigating the platform.
Event-Driven Infrastructure:
- AWS Lambda: Central to our serverless computing strategy, Lambda functions are triggered by various AWS service events, allowing for highly scalable, event-driven processing without managing infrastructure.
- Amazon S3 and CloudFront: S3's event notification feature serves as a source for Lambda triggers, enabling operations like processing uploaded documents in real-time. CloudFront’s global content delivery network ensures fast, secure delivery of web content.
- Amazon SQS and SNS (Simple Notification Service): SQS queues and SNS topics facilitate decoupled components within the architecture, managing message queues and push notifications that trigger downstream processes in an orderly, scalable manner.
- AWS Step Functions: Coordinates the multiple microservices and Lambda functions, orchestrating complicated workflows in response to events with ease and reliability.
Monitoring and Autoscaling:
- Amazon CloudWatch: Monitors the performance of AWS resources and applications in real time, enabling automated responses to system-wide performance changes based on predefined metrics and events.
- AWS Auto Scaling Groups (ASG): Automatically adjusts necessary compute resources to maintain performance and minimize costs, dynamically responding to fluctuations in demand.
Database and Caching Technologies:
- PostgreSQL for structured data storage, alongside DynamoDB for scalable NoSQL storage options that cater to different data models and access patterns inherent in an event-driven architecture.
- Redis: Provides high-performance caching to speed up dynamic data retrieval, crucial for maintaining fast response times in an event-driven system.

Team we built

Before embarking on this project, the client and BigCircle had been in collaboration for over a year. To tackle the specific needs of scaling the streaming platform, most of the team was carefully pieced together by us. For this undertaking, we assembled a comprehensive team, comprising a Product Architect, 3 Developers, 1 DevOps Engineer, 1 QA Engineer, and a Project Manager.

BigCircle expanded the client's team and spearheaded the development process.

We worked on

Web development

Cloud development & DevOps

Software Architecture Development

Quality Assurance

5. Results

The deployment of precise technological strategies has produced substantial and quantifiable benefits, greatly benefiting the client's operations. Through a thorough revamp of the system architecture, database management, and AI indexing services, we've successfully converted the platform into one that is highly scalable, efficient, and prepared for future advancements. The following outlines the primary results and impacts stemming from our strategic actions.

The transformative efforts on our platform have surpassed our expectations. The improvements in efficiency, scalability, and the cost reductions realized have significantly bolstered our position for future expansion. Our confidence in delivering unparalleled value to our users is higher than ever, owing to the skill and commitment of the project team. This initiative has been a pivotal milestone for our operations.

The client's Chief Technology Officer

6. Conclusion and Lessons Learned

Summary of Achievements

Our collaboration with the client led to numerous significant achievements that have fundamentally transformed their operational framework, including:

Shift to Microservices Architecture: Successfully transitioning the platform to a microservices architecture, which enabled scalability, flexibility, and ease of maintenance, thereby allowing it to efficiently meet both current and future demands.
Optimization of AI Indexing Service: Significantly reducing the processing time for research papers and enhancing the efficiency of the AI indexing service, improving user experience and platform reliability.
Database Management and Scalability: Implementing a managed database solution with Amazon RDS for PostgreSQL, which delivered enhanced performance, availability, and scalability, alongside cost-effective operation through optimized resource utilization.
Cost-Effective Scaling and Operational Efficiency: Achieving notable operational cost savings and ensuring the platform remains economically sustainable as it scales, courtesy of adopting serverless technologies and cloud-native solutions.

Lessons Learned

Throughout this project, we gleaned several critical insights that will inform our future endeavors and could serve as guiding principles for similar projects:

Iterative Migration and Testing is Crucial: The importance of adopting an iterative approach to transitioning architecture cannot be overstated. Gradual migration, coupled with parallel running and extensive testing phases, minimized disruptions and ensured a smooth transition process.
Importance of Monitoring and Optimization: Continuous monitoring and iterative optimization are vital for maintaining system performance and cost efficiency. Tools like Amazon RDS Performance Insights and Amazon CloudWatch were instrumental in achieving operational excellence.
Embrace Change Management: Effective communication and change management strategies were key to navigating the shift to new technologies and architectures. Engaging all stakeholders early and maintaining transparency around project goals, progress, and outcomes helped in managing expectations and fostering a culture of innovation.
Flexibility for Future Growth: Building a system with the future in mind involves creating a flexible, adaptable architecture that can easily integrate new features and technologies without significant overhauls, a lesson that shaped our approach from the outset.

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