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Subrat SaurabhAuthor of Kuch Woh Pal
Mastering the Machine: ML for the Real World explores the practical challenges and strategies for implementing machine learning systems beyond controlled research environments. While academic ML often focuses on clean datasets and benchmark accuracy, real-world applications must deal with messy, incomplete, and constantly evolving data. The book emphasizes that success in production ML is less about achieving the highest model accuracy and more about building systems that are scalable, reliable, interpretable, and aligned with business goals. Key themes include the importance of data quality and preprocessing, as most real-world effort goes into cleaning, balancing, and engineering features rather than model selection alone. The text highlights data drift, concept drift, and feedback loops, showing how models degrade over time without proper monitoring and retraining. It also covers model evaluation, stressing that accuracy is insufficient for imbalanced datasets and that fairness, interpretability, and business KPIs must guide decision-making. Overall, the work positions machine learning as not just a technical challenge but a socio-technical system requiring collaboration among data scientists, engineers, and domain experts.
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Dr. Rahul Sharma, Dr. Praveen Kumar Patidar, Dr. Sumit Sharma, Dr. Shivendra Dubey
Dr. Rahul Sharma - Assistant Professor - Senior Scale, Department of Artificial Intelligence & Machine Learning, Manipal University Jaipur, Jaipur, Rajasthan
Dr. Praveen Kumar Patidar - Associate Professor, Department of Computer Science and Engineering, Parul Institute of Technology, Parul University, Vadodara, Gujarat
Dr. Sumit Sharma - Head of the Department, Department of Computer Science and Engineering - Data Science, Oriental College of Technology, Bhopal, M.P.
Dr. Shivendra Dubey - Assistant Professor - Senior Scale, Department of Artificial Intelligence & Machine Learning, Manipal University Jaipur, Jaipur, Rajasthan
