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.
A practical guide to LangChain, the framework for building LLM-powered applications using composable chains, prompt templates, and the LangChain Expression Language.