Abstract

Artificial intelligence (AI) is poised to play an increasingly significant role in most organizations, enabling them to offer innovative new products and services, delivering value through automation, and maximizing returns through advanced predictive and prescriptive insights. However, deploying and operating AI at scale is often hindered by complexity, confusion, and operational inertia. Multiple definitions and perspectives of MLOps and other related concepts, such as DataOps, DevSecOps, GitOps, AI Engineering, and AI Lifecycle Management, have created a patchwork of standards and best practices, focused on only certain aspects of the broader challenge of developing and operationalizing AI capabilities. This has made it difficult for enterprise decision-makers to understand the complexity of AI operations, how different roles and teams fit together, and how to establish company-wide systems and processes to manage the development and deployment of AI technologies at scale. As organizations enter into the next phase of AI maturity, these challenges need to be addressed, so that the initial experimentation with pilot AI projects can be scaled into large-scale production-grade AI systems that deliver the benefits of AI capabilities to enterprises more efficiently and effectively. In this chapter, we first provide an overview of AI and its business value. The overview is followed by a high-level look into the machine learning (ML) lifecycle, where we introduce the concept of operationalizing intelligence, AI system parts, and the need for AI-enabled business infrastructures, before delving into the operationalizing of the ML lifecycle with MLOps. Next, we highlight key themes that form the basis of the subsequent chapters in this book. We then introduce the audience and structure of the book. Finally, we conclude with a summary that recaps the key lessons shared in this chapter. This helps set the foundation for a deep-dive exploration of the various aspects of MLOps, as an instantiation of the broader concept of operationalizing intelligence, in the rest of the book.

Keywords

  • AI Business Value
  • AI Operationalization
  • Enterprise AI Scaling
  • MLOps Lifecycle
  • AI Lifecycle Management
  • DevSecOps Integration
  • DataOps Practices
  • GitOps for AI
  • AI Engineering Standards
  • Production-Grade AI
  • AI System Infrastructure
  • Cross-Team Collaboration
  • AI Deployment Pipelines
  • Operationalizing Intelligence
  • ML System Complexity
  • AI Maturity Phases
  • Best Practices for MLOps
  • Scalable AI Systems
  • AI Governance Models
  • Strategic AI Enablement.

References

  1. 1. Ganti, V. K. A. T. (2019). Data Engineering Frameworks for Optimizing Community Health Surveillance Systems. Global Journal of Medical Case Reports, 1, 1255.
  2. 2. Maguluri, K. K., & Ganti, V. K. A. T. (2019). Predictive Analytics in Biologics: Improving Production Outcomes Using Big Data.
  3. 3. Polineni, T. N. S., & Ganti, V. K. A. T. (2019). Revolutionizing Patient Care and Digital Infrastructure: Integrating Cloud Computing and Advanced Data Engineering for Industry Innovation. World, 1, 1252.
  4. 4. Chava, K., Chakilam, C., Suura, S. R., & Recharla, M. (2021). Advancing Healthcare Innovation in 2021: Integrating AI, Digital Health Technologies, and Precision Medicine for Improved Patient Outcomes. Global Journal of Medical Case Reports, 1(1), 29–41. Retrieved from https://www.scipublications.com/journal/index.php/gjmcr/article/view/1294
  5. 5. Nuka, S. T., Annapareddy, V. N., Koppolu, H. K. R., & Kannan, S. (2021). Advancements in Smart Medical and Industrial Devices: Enhancing Efficiency and Connectivity with High-Speed Telecom Networks. Open Journal of Medical Sciences, 1(1), 55–72. Retrieved from https://www.scipublications.com/journal/index.php/ojms/article/view/1295
  6. 6. Adusupalli, B., Singireddy, S., Sriram, H. K., Kaulwar, P. K., & Malempati, M. (2021). Revolutionizing Risk Assessment and Financial Ecosystems with Smart Automation, Secure Digital Solutions, and Advanced Analytical Frameworks. Universal Journal of Finance and Economics, 1(1), 101–122. Retrieved from https://www.scipublications.com/journal/index.php/ujfe/article/view/1297
  7. 7. Gadi, A. L., Kannan, S., Nandan, B. P., Komaragiri, V. B., & Singireddy, S. (2021). Advanced Computational Technologies in Vehicle Production, Digital Connectivity, and Sustainable Transportation: Innovations in Intelligent Systems, Eco-Friendly Manufacturing, and Financial Optimization. Universal Journal of Finance and Economics, 1(1), 87–100. Retrieved from https://www.scipublications.com/journal/index.php/ujfe/article/view/1296
  8. 8. Singireddy, J., Dodda, A., Burugulla, J. K. R., Paleti, S., & Challa, K. (2021). Innovative Financial Technologies: Strengthening Compliance, Secure Transactions, and Intelligent Advisory Systems Through AI-Driven Automation and Scalable Data Architectures. Universal Journal of Finance and Economics, 1(1), 123–143. Retrieved from https://www.scipublications.com/journal/index.php/ujfe/article/view/1298
  9. 9. Anil Lokesh Gadi. (2021). The Future of Automotive Mobility: Integrating Cloud-Based Connected Services for Sustainable and Autonomous Transportation. International Journal on Recent and Innovation Trends in Computing and Communication, 9(12), 179–187. Retrieved from https://ijritcc.org/index.php/ijritcc/article/view/11557
  10. 10. Balaji Adusupalli. (2021). Multi-Agent Advisory Networks: Redefining Insurance Consulting with Collaborative Agentic AI Systems. Journal of International Crisis and Risk Communication Research , 45–67. Retrieved from https://jicrcr.com/index.php/jicrcr/article/view/2969
  11. 11. Pallav Kumar Kaulwar. (2021). From Code to Counsel: Deep Learning and Data Engineering Synergy for Intelligent Tax Strategy Generation. Journal of International Crisis and Risk Communication Research , 1–20. Retrieved from https://jicrcr.com/index.php/jicrcr/article/view/2967
  12. 12. Somepalli, S., & Siramgari, D. (2020). Unveiling the Power of Granular Data: Enhancing Holistic Analysis in Utility Management. Zenodo. https://doi.org/10.5281/ZENODO.14436211
  13. 13. Ganesan, P. (2021). Leveraging NLP and AI for Advanced Chatbot Automation in Mobile and Web Applications. European Journal of Advances in Engineering and Technology, 8(3), 80-83.
  14. 14. Somepalli, S. (2019). Navigating the Cloudscape: Tailoring SaaS, IaaS, and PaaS Solutions to Optimize Water, Electricity, and Gas Utility Operations. Zenodo. https://doi.org/10.5281/ZENODO.14933534
  15. 15. Ganesan, P. (2021). Cloud Migration Techniques for Enhancing Critical Public Services: Mobile Cloud-Based Big Healthcare Data Processing in Smart Cities. Journal of Scientific and Engineering Research, 8(8), 236-244.
  16. 16. Somepalli, S. (2021). Dynamic Pricing and its Impact on the Utility Industry: Adoption and Benefits. Zenodo. https://doi.org/10.5281/ZENODO.14933981
  17. 17. Ganesan, P. (2020). Balancing Ethics in AI: Overcoming Bias, Enhancing Transparency, and Ensuring Accountability. North American Journal of Engineering Research, 1(1).
  18. 18. Satyaveda Somepalli. (2020). Modernizing Utility Metering Infrastructure: Exploring Cost-Effective Solutions for Enhanced Efficiency. European Journal of Advances in Engineering and Technology. https://doi.org/10.5281/ZENODO.13837482
  19. 19. Ganesan, P. (2020). PUBLIC CLOUD IN MULTI-CLOUD STRATEGIES INTEGRATION AND MANAGEMENT.