LLM Inference Caching: Pay for Computation Once
LLM inference caching reuses past computations to cut costs and latency. It avoids reprocessing shared system prompts or serves full answers for common queries without hitting the model. The footgun: semantic caches can return a "similar" but incorrect answer.
Git-Based CI Triggers: Automating on Events
Think of Git events like `push` or `pull_request` as the "play" button for your automation. This is how CI systems automatically run tests on new code. The footgun is using broad triggers, like `push` on all branches, which causes costly and redundant runs.
Dev Containers: Your Dev Environment as Code
A dev container packages your entire development environment—tools, libraries, and settings—into a single, portable container. Use it to standardize team environments, simplify onboarding, and ensure consistency between local dev and CI.
Python Virtual Environments: Isolate Project Dependencies
A Python virtual environment is a self-contained directory with its own Python interpreter and packages, preventing dependency conflicts between projects. The biggest mistake is checking the environment folder into source control; it's disposable and meant to…
Great Expectations: Unit Tests for Your Data
Great Expectations brings unit testing to your data, letting you assert what a dataset should look like. It validates data within a pipeline, preventing bad data from corrupting models or reports.
CD4ML: Automating ML from Data to Deployment
CD4ML extends CI/CD to manage ML's three axes of change: code, data, and models. It automates the entire lifecycle, enabling reliable updates for systems like sales forecasting.