Course Objective & Scope
Modern AI and data projects constantly interact with APIs—fetching records from internal data services, querying external providers, calling LLM endpoints such as OpenAI, Anthropic or self-hosted vLLM, and exposing results back to downstream applications. This course teaches both how to write resilient Python API clients and how to expose clean REST services.
Participants build synchronous and asynchronous clients with requests and httpx, handle OAuth2/API Key auth, pagination, exponential backoff (tenacity) and streaming responses, and wrap their own Python logic into an OpenAPI-documented service. They leave with a reusable Python SDK/client template and an API integration checklist.
Who Should Attend?
- Data scientists and analysts connecting external services, data sources and LLM APIs to their workflows
- Python developers building AI integrations and backend glue layers
- Data and software engineers creating automation and data ingestion pipelines
- Prerequisite: basic Python knowledge (functions, dictionaries and exception handling)
Key Takeaways
- Interpret and apply HTTP methods, status codes, headers, JSON schemas and OpenAPI specifications
- Write connection-pooled, high-throughput API clients with
requests.Sessionandhttpx.AsyncClient - Handle rate limits (HTTP 429), timeouts and transient network failures using exponential backoff
- Integrate LLM and AI APIs including Server-Sent Events (SSE) streaming and structured outputs
- Expose a Python workflow as a schema-validated, self-documented REST API endpoint
Curriculum
HTTP, REST Architecture and Data Formats
- URL structure, query/path parameters, HTTP methods, status codes and idempotency
- JSON serialization, validating responses with Pydantic and reading OpenAPI/Swagger specs
Building Resilient API Clients in Python
requests.Session, connection pooling and explicit timeout configuration- API Keys, Bearer Token / OAuth2 flows, secret handling and cursor/offset pagination
- Retries, exponential backoff and rate-limit (
Retry-After) handling withtenacity
Asynchronous Calls and LLM/AI API Integration
- Sending concurrent requests safely with
httpx.AsyncClientandasyncio.Semaphore - Handling streaming (SSE), batch endpoints and token/cost tracking in LLM and embedding APIs
Exposing REST Endpoints and Webhook Receivers
- Wrapping Python data and AI functions as clean REST endpoints
- Receiving webhooks, verifying HMAC signatures and standardising error responses
API Testing, Mocking and Internal SDK Design
- Writing deterministic tests by mocking external APIs with
pytestandrespx/responses - Structuring a clean, reusable Python client wrapper (SDK) for internal services
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