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From developer to Python architect: CPython internals, performance engineering, distributed systems, and a multi agent AI platform.
8 modules · 48 lessons
CPython internals and performance engineering, system design and distributed architecture, event driven systems and microservices, advanced testing reliability and observability, packaging supply chain and engineering leadership, a minor project on performance engineering, advanced AI engineering and LLMOps, and a major project architecting a distributed AI platform.
This is the final stage of the Ucanly Python Programming track, where developers become Python architects. Built for practitioners with hands on experience shipping tested FastAPI services with PostgreSQL, async, and Docker, this program goes deep into CPython internals, memory management, the GIL, and native extensions with Cython and Rust bindings. You will design distributed systems with Kafka and gRPC, event driven microservices with sagas and idempotency, and apply property based testing, load testing, and OpenTelemetry observability. In your week six minor project, you will take a genuinely slow Python service to production speed through profiling, algorithmic fixes, an async rewrite, and native extensions, proven under real load. You will also build packages, lead code review and technical standards, and move into advanced AI engineering: production RAG, multi agent systems with LangGraph, evaluation pipelines, and guardrails. You will graduate having architected a distributed, event driven Python platform in your week twelve major project, with microservices, a multi agent AI layer, full observability, evaluation and guardrails, Kubernetes deployment, and a defended architecture document.
Complete this course to earn a verified Ucanly certificate you can add to your profile, share on LinkedIn, and showcase to employers as proof of the skills you've built.
It is recommended, but hands on experience shipping tested FastAPI services with PostgreSQL, async, and Docker is sufficient to start.
Yes, your minor project takes a genuinely slow Python service to production speed through profiling, algorithmic fixes, an async rewrite, and native extensions, with load tested proof of the improvement.
Production RAG architectures, multi agent systems with LangGraph, evaluation pipelines, guardrails, and cost and latency engineering.
A distributed, event driven Python platform with microservices, a multi agent AI layer, full observability, evaluation and guardrails, Kubernetes deployment, and a defended architecture document.