Fully-trained and optimized machine learning models tailored to the client's specific needs, such as predictive analytics, classification, or regression tasks. Formats: Model files (e.g., .h5, .pkl), configuration files. Quantities: One model per project scope. Revisions: Up to three iterations based on client feedback.

End-to-end data pipeline setup, including data preprocessing, feature engineering, and integration scripts to seamlessly incorporate the ML model into the client's existing systems. Formats: Python scripts (.py), pipeline configuration files, detailed documentation (e.g., .pdf, .md). Quantities: One comprehensive pipeline setup. Revisions: Two revisions to ensure seamless integration and functionality.

Detailed report evaluating model performance with metrics such as accuracy, precision, recall, and F1-score, along with recommendations for further improvements and deployment strategies. Formats: PDF report, supplementary data files (e.g., .csv, .xlsx), visual charts (e.g., .png, .svg). Quantities: One detailed report. Revisions: One revision to address client queries and ensure clarity.