Following are the stages in the lifecycle of a natural language processing (NLP) project:

• Data Collection: The procedure of collecting, measuring, and evaluating correct insights for research using established approved procedures is referred to as data collection.
• Data Cleaning: The practice of correcting or deleting incorrect, corrupted, IMPROPERLY formatted, duplicate, or incomplete data from a dataset is KNOWN as data cleaning.
• Data Pre-Processing: The process of CONVERTING raw data into a comprehensible format is known as data preparation.
• Feature Engineering: Feature engineering is the process of extracting features (characteristics, qualities, and attributes) from raw data using domain expertise.
• Data Modeling: The practice of examining data OBJECTS and their relationships with other things is known as data modelling. It's utilised to look into the data requirements for various business activities.
• Model Evaluation: Model evaluation is an important step in the creation of a model. It aids in the selection of the best model to represent our data and the prediction of how well the chosen model will perform in the future.
• Model Deployment: The technical task of exposing an ML model to real-world use is known as model deployment.
• MONITORING and Updating: The activity of measuring and analysing production model performance to ensure acceptable quality as defined by the use case is known as machine learning monitoring. It delivers alerts about performance difficulties and assists in diagnosing and resolving the core cause.