Microleap AI Food Detector Platform

Bài toán

Microleap was scaling an AI food detection product where inference quality, processing throughput, and deployment consistency all had to improve in parallel. The platform was constrained by uneven environment setups and weak service contracts between ingestion, inference, and metadata pipelines.

Trách nhiệm

• Implemented backend services for asynchronous image processing and metadata enrichment.
• Defined service boundaries and message contracts across ingestion, inference, and retrieval layers.
• Contributed deployment hardening for multi-environment CI/CD workflows.

Điểm nhấn kiến trúc

• Queue-first workload orchestration to smooth burst traffic and stabilize inference workers.
• Vector search integration with Qdrant to support more accurate similarity lookup in detection flows.
• Kubernetes deployment templates and health checks to reduce environment drift and release surprises.

Kết quả triển khai

• Inference throughput improved with clearer asynchronous execution boundaries.
• Production deploys became more predictable across staging and live clusters.
• Team velocity improved due to cleaner component ownership and fewer integration regressions.

Bài học rút ra

• AI product speed depends as much on delivery architecture as on model quality.
• Environment parity eliminates a large class of avoidable release incidents.
• Event contracts should be treated as product interfaces with strict ownership and versioning discipline.

Đóng góp và tác động chính

Tech stack

Golang, Azure Functions, Qdrant, Docker, Kubernetes, PostgreSQL