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DTS deployment consists of two primary components: Software Development Kit (SDK) and Web Service.
For this solution, DTS adopted an SDK-First development approach that uses Data Science (DS) functions, which are implemented as a library that integrates into a larger system. The larger system as NBA is a real-time support platform, tightly coupled with the business infrastructure to leverage PSQN predictions. The PSQN SDK is designed to align with how the NBA Application Programming Interface (API) web service would consume it. DTS’s primary goal for the SDK is to expose functionalities related to article retrieval from the indexed data within the PGVector database as an offline operation. It facilitates query inference, which the PSQN web service uses to generate article predictions. Creating an SDK involves close collaboration with the DS team, understanding the source code, and converting it into an efficient and optimized ‘consumable package’ for installation within the web service dependencies container image.
PSQN SDK releases are decoupled from API releases. This approach provides flexibility for the PSQN core DS development team to keep refining the model without frequent web service updates, if the API contract remains unchanged. The SDK undergoes rigorous unit-tests, integration-tests, performance testing, security, and quality checks.
The web service provides an API-endpoint for PSQN model inference (such as POST /api/v0/psqn-retrieval). The NBA application side invokes the endpoint to retrieve the PSQN predictions as a list of article data items in a JSON serializable format.
The following figure illustrates the major solution-deployment components of the PSQN web service:
DTS's deployment process for the PSQN web service relies on a set of essential DevOps tools. These tools and infrastructure are used across other tools to check the code quality and security checks. The key components include: