• SAHANA

In recent years, machine learning (ML) has transitioned from an academic focus to a vital tool for solving real-world business challenges. However, deploying ML models into production presents numerous practical issues across various industries and applications. This survey reviews published case studies to highlight key concerns at each stage of the ML deployment workflow, aiming to inform a research agenda that addresses these challenges. ML, a subset of artificial intelligence (AI), develops algorithms that learn from data and generalize to new situations using statistical techniques. Among these, Artificial Neural Networks (ANNs) have gained prominence for their success in tasks like computer vision and natural language processing, often praised for mimicking the structure of biological neurons. Yet, this work argues that despite their effectiveness, ML methods cannot be fully classified as scientific from a methodological standpoint, as they typically lack causal explanations behind their predictions. For instance, a neural network may predict creditworthiness based on financial indicators like income and credit history, but without explaining the underlying reasons. Emerging techniques such as reinforcement and imitation learning offer new directions, as they begin to mirror cognitive aspects of human learning.

Machine learning, Neural networks, AI

"ISSUES AND CHALLENGES OF MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 8, page no.144-146, August-2025, Available :http://www.jetir.org/papers/JETIRHB06029.pdf

"ISSUES AND CHALLENGES OF MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 8, page no. pp144-146, August-2025, Available at : http://www.jetir.org/papers/JETIRHB06029.pdf