Citation :

Venkata Sathya Kumar Koppisetti, 2024. "Machine Learning at Scale: Powering Insights and Innovations" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 2, Issue 2: 56-61.

Abstract :

Large-scale processing power, enabled by machine learning, has become a pivotal device of choice for exploring previously unexplored domains. This article explores the frameworks, obstacles, and improvements in scaling machine learning systems, focusing on solutions that allow to achieve both the throughput and the reliability of the system when working with large amounts of data. The debate covers parallel computing platforms, optimization methods, and system designs that allow complex computation. By intersecting the real-life examples from business and academy in the essay, the practical applications and benefits of machine learning on a big scale are demonstrated. Despite the promise of AI, essential challenges such as data heterogeneity, model interpretability, and computational constraints are tackled and, as a result, state-of-the-art overview and future directions of the main areas are clarified.

References :

[1] Jason Chong, What is Feature Scaling & Why is it Important in Machine Learning?, Medium, 2020. https://towardsdatascience.com/what-is-feature-scaling-why-is-it-important-in-machine-learning-2854ae877048

[2] Vikram Singh, Evaluation Metrics in Machine Learning, Shiksha, 2023. https://www.shiksha.com/online-courses/articles/evaluating-a-machine-learning-algorithm/

[3] Hadoop Ecosystem, Geeksforgeeks, 2024. https://www.geeksforgeeks.org/hadoop-ecosystem/

[4] Venkata Sathya Kumar Koppisetti, 2024. "The Future of Remote Collaboration: Leveraging AR and VR for Teamwork" ESP International Journal of Advancements in Computational Technology (ESP-IJACT) Volume 2, Issue 1: 56-65.

[5] Venkata Sathya Kumar Koppisetti, "Automation of Triangulation, Inter-Company, or Intra-Company Procurement in SAP SCM," International Journal of Computer Trends and Technology, vol. 71, no. 9, pp. 7-14, 2023. Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I9P102

[6] "DIFFERENTIAL PRIVACY TECHNIQUES IN MACHINE LEARNING FOR ENHANCED PRIVACY PRESERVATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.b148-b153, February-2024, Available: http://www.jetir.org/papers/JETIR2402116.pdf

[7] Sridhar Selvaraj, 2024. "SAP Supply Chain with Industry 4.0" ESP International Journal of Advancements in Computational Technology (ESP-IJACT) Volume 2, Issue 1: 44-48.

[8] Janaha Vivek, Top 5 Challenges When Scaling Machine Learning, Zucisystems. https://www.zucisystems.com/blog/top-5-challenges-when-scaling-machine-learning/

[9] Venkata Sathya Kumar Koppisetti, 2024. "The Role of Explainable AI in Building Trustworthy Machine Learning Systems" ESP International Journal of Advancements in Science & Technology (ESP-IJAST) Volume 2, Issue 2: 16-21.

[10] Kushal Walia, 2024. “Scalable AI Models through Cloud Infrastructure” ESP International Journal of Advancements in Computational Technology (ESP-IJACT) Volume 2, Issue 2: 1-7

[11] Venkata Sathya Kumar Koppisetti, "Automation of Vendor Invoice Process with OpenText Vendor Invoice Management ," International Journal of Computer Trends and Technology, vol. 71, no. 8, pp. 71-75, 2023. Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I8P111

[12] Kushal Walia, 2024. "Accelerating AI and Machine Learning in the Cloud: The Role of Semiconductor Technologies" ESP International Journal of Advancements in Computational Technology (ESP-IJACT) Volume 2, Issue 2: 34-41.

[13] Jabin Geevarghese George (2024). Empowering Fintech Innovation: A Strategic Guide to Generative AI Integration and Hybrid Cloud Adoption, International Research Journal of Modernization in Engineering Technology and Science, Volume 6, Issue 4: 32-40.

[14] Sridhar Selvaraj, 2024. "Futuristic SAP Fiori Dominance" ESP International Journal of Advancements in Computational Technology (ESP-IJACT) Volume 2, Issue 1: 32-37.

Keywords :

Mental health, Chatbot, Depression, Therapy, Patient, Artificial Intelligence.