Innovation Lab Service Fabric

Cost-effective serverless platform on Microsoft Azure for rapid deployment of machine learning services with various building blocks and HTTP APIs.

Client
Advanced Solutions
Date
June 1, 2019
Service
Software Engineering
Location
Louisville - KY (Remote)
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Advanced Solutions

Challenge

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The primary objective of this project was to establish a versatile backend-as-a-service internal platform, facilitating rapid development for both internal initiatives and customer-facing projects. Some of faced challenges are the following:

  • Integration Complexity: Coordinating various components and services within the serverless architecture might pose integration challenges.
  • Security Concerns: Ensuring robust security protocols to protect data and APIs from potential breaches and attacks.
  • Scalability Issues: Managing scalability, especially during sudden spikes in traffic or data volume, requires careful planning.
  • Optimizing Costs: Balancing the usage of cloud resources to avoid unnecessary costs and optimizing the platform for cost-effectiveness.
  • Versioning and Compatibility: Handling versioning issues, especially when updates occur in machine learning models or APIs, ensuring backward compatibility.
  • Monitoring and Debugging: Implementing comprehensive monitoring tools for performance tracking and effective debugging in a serverless environment.
  • Regulatory Compliance: Adhering to data protection regulations and compliance standards specific to the region of operation.
  • Vendor Lock-in: Avoiding excessive reliance on specific cloud service providers to maintain flexibility and prevent vendor lock-in.
    • Advanced Solutions

    Project Results

    To overcome these challenges and construct a resilient backend-as-a-service platform capable of powering a multitude of solutions with cost-efficiency and scalability, several strategic measures were implemented. Integration complexities were mitigated through meticulous planning and the adoption of standardized protocols. Security concerns were addressed through robust encryption, access controls, and continuous monitoring for potential vulnerabilities. Scalability was achieved by leveraging auto-scaling features and optimizing resource utilization. Cost optimization was ensured through careful resource allocation and periodic cost analysis. Versioning and compatibility issues were resolved by implementing version control mechanisms and API versioning strategies. Comprehensive monitoring and debugging tools were integrated to facilitate real-time performance tracking and swift issue resolution. Regulatory compliance was adhered to through rigorous data protection measures and adherence to regional compliance standards. To prevent vendor lock-in, a multi-cloud approach was adopted, allowing flexibility in selecting cloud providers. Through these measures, a resilient backend-as-a-service platform was successfully developed, poised to support diverse solutions with efficiency, security, and scalability, all while maintaining cost-effectiveness.