Gin-config is Google’s lightweight configuration framework that uses decorators and .gin files to make Python functions and classes configurable without boilerplate code.
Learn how BERTScore evaluates generated text with contextual embeddings, when to use it instead of ROUGE, BLEU, or LLM-as-judge, and how to wire it into watsonx.ai evaluation workflows.
How Ollama makes local LLMs practical: setup, the Python client and OpenAI-compatible API, when local inference beats hosted models, and how it pairs with watsonx.ai in hybrid architectures.
A practical guide to Folium for interactive Leaflet.js maps in Python: markers, choropleths, heatmaps, when to choose it over Plotly or pydeck, and how to publish maps from data pipelines.
A practical guide to LangGraph, the library for building stateful, multi-actor LLM applications using graph-based workflows with cycles, branching, and persistence.
A practical guide to LlamaIndex, the data framework for building retrieval-augmented generation (RAG) applications that connect custom data sources to LLMs.
Why XGBoost remains the default for tabular machine learning: a practical guide with early stopping, GPU training, and SHAP explainability, plus an honest comparison with LightGBM, CatBoost, and neural networks.