NLP Tooling and Practical Pipelines
NLP Tooling and Practical Pipelines In natural language processing, good tooling saves time and reduces errors. A practical pipeline shows how data moves from collection to a deployed model. It includes data collection, cleaning, feature extraction, model training, evaluation, deployment, and monitoring. A small, transparent toolset is easier to learn and safer for teams. Start with a simple plan. Define your goal, know where the data comes from, and set privacy rules. Choose a few core components: data versioning, an experiment log, and a lightweight workflow engine. Tools like DVC, MLflow, and Airflow or Prefect are common choices, but you can start with a smaller setup. ...