Smart Data Science Workflows: AI Techniques in Python for Automation

Abstract

In the rapidly evolving landscape of data science, automation has become a crucial factor in enhancing productivity and ensuring efficiency in workflows. This paper explores the integration of Artificial Intelligence (AI) techniques within Python-based data science workflows to streamline processes, reduce manual intervention, and accelerate decision-making. We examine various automation tools and libraries available in Python, such as Pandas, Scikit-learn, and Airflow, which facilitate the creation of robust data pipelines and enable the implementation of machine learning models. By showcasing practical applications and real-world case studies, this work highlights how AI can automate data cleaning, preprocessing, model training, and deployment. The findings suggest that employing AI techniques not only optimizes data handling but also fosters a more agile and responsive data science environment. Ultimately, this paper serves as a guide for data scientists seeking to leverage Python for automating complex workflows, thereby enhancing overall project outcomes and driving innovation.