Articles
Azure-Based Adaptive Predictive Analytics for Dynamic Business Intelligence in AgriTech Systems
Abstract
Adaptive Predictive Analytics Pipelines on Microsoft Azure: A Machine Learning Approach for Dynamic Business Intelligence in AgriTech Systems
Business intelligence systems, including predictive analytics repositories, are essential for effective decision support in agriculture and agri-tech. Yet, many existing solutions are unable to adapt to changing conditions. Recent advances in machine learning (ML) provide opportunities to alleviate some of the associated problems. On the Microsoft Azure platform, a set of services built around data lakes and ML models enables the implementation of adaptive predictive analytics pipelines—data-processing assemblies that optimize performance by continuously learning from available data. These pipelines can react to changes, such as the emergence of new patterns, by automatically altering feature engineering processes, retraining models using updated sources, or providing predictions with new data representations, characteristics, or horizons.
The architecture and implementation of these adaptive predictive analytics pipelines are described in detail, with descriptions of the pipelines’ general structure and operation, along with a case study focused on the Agri-Tech Risk Mitigation Solutions (AT-RMS) predictive analytics service. AT-RMS assists executive-level actors in the agriculture and insurance sectors with strategic-level decision-making, and the implementation leverages Azure to support infrastructure-as-code and operational governance.
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 7 No. 4 (2024): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
14520-14533 |
Published |
July 18, 2024 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Nareddy Abhireddy, Dr. Emily Carter (2024). Azure-Based Adaptive Predictive Analytics for Dynamic Business Intelligence in AgriTech Systems. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 7 No. 4 (2024): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 14520-14533. https://doi.org/10.15662/IJAESIT.2024.0704008 |
References
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34. Mattaparthi, R. (2023). Connected Fleet Intelligence: Edge-Centric Analytics and Computer Vision for Predictive Manufacturing and Asset Resilience. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(5), 9077-9088.
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37. Mangala, N. (2022). Real-Time Data Quality Monitoring and Gating Frameworks in Cloud-Based Data Pipelines. International Journal of Research and Applied Innovations, 5(6), 8197-8219.
38. Peddi, R. K. (2021). Optimizing Case Management Workflows in Global Data Center Colocation Services. Universal Journal of Computer Sciences and Communications, 1(1), 1-21.
39. Loganathan, R. (2021). Integrated Risk and Compliance Frameworks for Global Data Center Operations: A Governance-Centric Approach. Universal Journal of Computer Sciences and Communications, 1(1), 1-26.
40. Amistapuram, K. (2021). Digital Transformation in Insurance: Migrating Enterprise Policy Systems to. NET Core. Universal Journal of Computer Sciences and Communications, 1(1), 1-17.
41. Reddy, V. A. R. (2023). Predictive Healthcare Administration Using Advanced Payer Analytics and Population Health Data Engineering. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(2), 7967-7978.
42. Gottimukkala, V. R. R. (2022). Licensing Innovation in the Financial Messaging Ecosystem: Business Models and Global Compliance Impact. International Journal of Scientific Research and Modern Technology, 1(12), 177-186.
43. Mangalampalli, B. M. (2022). Automated Invoice Validation Systems Using Advanced SQL Analytics in Healthcare Insurance. Front Health Inform, 11.
44. Reddy, V. A. R. (2021). Challenges in Standardizing Member Eligibility Data Across Multi-Payer Healthcare Ecosystems. International Journal of Medical Toxicology and Legal Medicine, 24(3), 1-19.
45. Davuluri, P. S. L. (2023). AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems (December 15, 2023).
46. Yandamuri, U. S. (2021). A Comparative Study of Traditional Reporting Systems versus Real-Time Analytics Dashboards in Enterprise Operations. Universal Journal of Business and Management, 1(1), 1-13.
47. Divya, V., & Bandi, V. K. (2023). Cloud-Native Model Lifecycle Management for Enterprise AI Systems. International Journal of Scientific Research and Modern Technology, 78.
48. Kolla, S. K., & Reddy, V. A. R. (2023). Deep Learning Architectures For Multimodal Medical Data Integration. South Eastern European Journal of Public Health, 248–260.
49. Bandi, V. D. V. K. (2023, November). Production-Grade Machine Learning Pipelines For Healthcare Predictive Analytics. South Eastern European Journal of Public Health, 189–205.
50. Kolla, S. H., & Loganathan, R. (2023). Cloud-Native Deep Learning Architectures For Secure Generative AI Deployment In Enterprise Workflow Platforms. Journal of International Crisis and Risk Communication Research, 603-618.
51. Mangala, N. (2021). Optimizing Large-Scale ETL Pipelines Using Medallion Architecture on Azure Data Lake. Journal of Artificial Intelligence and Big Data, 1(1), 1-20.
52. Paleti, S. (2023). AI-driven innovations in banking: Enhancing risk compliance through advanced data engineering. Available at SSRN, 5244840.
53. Reddy, V. A. R. (2023). Orchestrating the Future Autonomous Healthcare Data Pipeline Management through Agentic AI Architectures. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(2), 7979-7992.
54. Kolla, S. K. (2023). Learning Health Systems Machine Intelligence for Clinical Prediction and Healthcare Optimization. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(2), 7955-7966.
55. Valiki, D., & Segireddy, A. R. (2023). Deep Learning Architectures Deployed on Cloud Platforms for Dynamic Financial Risk Evaluation and Market Prediction. American International Journal of Computer Science and Technology, 5(5), 12-24.
56. Amistapuram, K. Energy-Efficient System Design for High-Volume Insurance Applications in Cloud-Native Environments. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), DOI, 10.
57. Kaulwar, P. K., Pamisetty, A., Mashetty, S., Adusupalli, B., & Pandiri, L. (2023). Harnessing intelligent systems and secure digital infrastructure for optimizing housing finance, risk mitigation, and enterprise supply networks. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 372-402.
58. DAVULURI, P. N. (2022). Cloud-Native Data Platform Modernization for Regulatory Compliance in Global Banking. Kurdish Studies.
59. Kolla, S. H. (2023). Large Language Model-Driven Enterprise Service Intelligence for Digital Workflow Transformation. International Journal of Research and Applied Innovations, 6(1), 8380-8391.
60. Loganathan, R. (2022). Converging Security Architecture and Compliance Management in Enterprise Data Center Ecosystems: A Unified Control Framework. International Journal of Scientific Research and Modern Technology, 1(12), 295-312.
61. Yandamuri, U. S. (2022). Cloud-Based Data Integration Architectures for Scalable Enterprise Analytics. International Journal of Intelligent Systems and Applications in Engineering, 10, 472-483.
62. Kolla, T., & Kolla, S. K. (2023). FHIR-Based Real-Time Healthcare Analytics using Unsupervised Learning. International Journal of Future Innovative Science and Technology (IJFIST), 6(6), 11751.
63. Mangala, N. (2022). Implementing Databricks Unity Catalog For Centralized Data Governance In Multi-Business-Unitenterprises. Journal of International Crisis and Risk Communication Research, 101-122.
64. Bandi, V. D. V. K. (2023). MLOps frameworks for reliable model deployment in cloud data platforms. Journal of Artificial Intelligence and Big Data, 3(1), 84-101.
65. Mangalampalli, B. M. (2021). Scalable Data Warehouse Architecture for Population Health Management and Predictive Analytics. World Journal of Clinical Medicine Research, 1(1), 1-18.
66. Mashetty, S. (2023). A Comparative Analysis of Patented Technologies Supporting Mortgage and Housing Finance. Available at SSRN 5249181.
67. Meda, R. (2023). Developing AI-Powered Virtual Color Consultation Tools for Retail and Professional Customers. Journal for ReAttach Therapy and Developmental Diversities. https://doi. org/10.53555/jrtdd. v6i10s (2), 3577.
68. Paleti, S. (2023). Trust layers: AI-augmented multi-layer risk compliance engines for next-gen banking infrastructure. Available at SSRN, 5221895.
69. Challa, K. (2023). Transforming Travel Benefits through Generative AI: A Machine Learning Perspective on Enhancing Personalized Consumer Experiences. Educational Administration: Theory and Practice. Green Publication. Educational Administration: Theory and Practice. Green Publication. https://doi. org/10.53555/kuey. v29i4, 9241.
2. Challa, K. (2023). Optimizing Financial Forecasting Using Cloud Based Machine Learning Models. Journal for ReAttach Therapy and Developmental Diversities. https://doi. org/10.53555/jrtdd. v6i10s (2), 3565.
3. Inala, R. (2023). AI-powered investment decision support systems: Building smart data products with embedded governance controls. Journal for ReAttach Therapy and Developmental Diversities, 6(10), 2251-2266.
4. Adusupalli, B. (2023). DevOps-Enabled Tax Intelligence: A Scalable Architecture for Real-Time Compliance in Insurance Advisory. Journal for Reattach Therapy and Development Diversities. Green Publication. https://doi. org/10.53555/jrtdd. v6i10s (2), 358.
5. Meda, R. (2023). Data Engineering Architectures for Scalable AI in Paint Manufacturing Operations. European data science journal.
6. Paleti, S. (2022). Fusion bank: Integrating AI-driven financial innovations with risk-aware data engineering in modern banking. Decision Making, 2326, 9865.
7. Vijaya Rama Raju Gottimukkala, “Energy-Efficient Design Patterns for Large-Scale Banking Applications Deployed on AWS Cloud,” International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2020.91225
8. Mattaparthi, R. (2022). Engineering Predictive Industrial Systems Through IoT-Driven Asset Monitoring and Machine Learning Prognostics. International Journal of Future Innovative Science and Technology (IJFIST), 5(1), 7790.
9. Kolla, S. K., & Reddy, V. A. R. (2023). Deep Learning Architectures For Multimodal Medical Data Integration. South Eastern European Journal of Public Health, 248-260.
10. Mangalampalli, B. M. (2023). AI-Driven Anomaly Detection in Healthcare Claims Data: A Business Intelligence Perspective. Journal of Rare Cardiovascular Diseases.
11. Nagabhyru, K. C. (2022). Bridging Traditional ETL Pipelines with AI Enhanced Data Workflows: Foundations of Intelligent Automation in Data Engineering. Available at SSRN 5505199.
12. Gottimukkala, V. R. R. (2021). Digital Signal Processing Challenges in Financial Messaging Systems: Case Studies in High-Volume SWIFT Flows. Universal Journal of Finance and Economics.
13. Garapati, R. S. (2023). Optimizing Energy Consumption in Smart Build-ings Through Web-Integrated AI and Cloud-Driven Control Systems.
14. Kaulwar, P. K., Pamisetty, A., Mashetty, S., Adusupalli, B., & Pandiri, L. (2023). Harnessing intelligent systems and secure digital infrastructure for optimizing housing finance, risk mitigation, and enterprise supply networks. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 372-402.
15. Aitha, A. R. (2022). Cloud Native ETL Pipelines for Real Time Claims Processing in Large Scale Insurers. Universal Journal of Business and Management.
16. Nagabhyru, K. C., & Engineer, S. D. (2023). Unifying Data Engineering and Machine Learning Pipelines: An Enterprise Roadmap to Automated Model Deployment.
17. Inala, R. (2023). Revolutionizing Customer Master Data in Insurance Technology Platforms: An AI and MDM Architecture Perspective. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 579-606.
18. Garapati, R. S. (2022). Web-Centric Cloud Framework for Real-Time Monitoring and Risk Prediction in Clinical Trials Using Machine Learning. Current Research in Public Health, 2, 1346.
19. Kolla, T. (2023). Predictive ETL Failure Detection in Healthcare Data Pipelines Using Anomaly Detection Algorithms. International Journal of Medical Toxicology & Legal Medicine.
20. Nagabhyru, K. C. (2023). From Data Silos to Knowledge Graphs: Architecting CrossEnterprise AI Solutions for Scalability and Trust. Available at SSRN.
21. Inala, R. (2023). Big Data Architectures for Modernizing Customer Master Systems in Group Insurance and Retirement Planning. Educational Administration: Theory and Practice, 29(4), 5493-5505.
22. Challa, K. (2023). Dynamic Neural Network Architectures for Real-Time Fraud Detection in Digital Payment Systems Using Machine Learning and Generative AI. Nanotechnology Perceptions.
23. Garapati, R. S. (2022). AI-Augmented Virtual Health Assistant: A Web-Based Solution for Personalized Medication Management and Patient Engagement. Available at SSRN.
24. Mattaparthi, R. (2023). Deep Learning-Driven Combustion Anomaly Detection in Diesel Powertrains: A Multi-Sensor Fusion Approach for Real-Time ECM Adaptation. International Journal of Intelligent Systems and Applications in Engineering, 11, 1084.
25. Segireddy, A. R. (2020). Cloud Migration Strategies for High-Volume Financial Messaging Systems.
26. Amistapuram, K. (2023). Privacy-Preserving Machine Learning Models for Sensitive Customer Data in Insurance Systems. Educational Administration: Theory and Practice, 29(4), 5950-5958.
27. Davuluri, P. S. L. N. (2023). Integrating artificial intelligence into event-driven financial crime compliance platforms. International Journal of Finance, 36(6), 707-736.
28. Adusupalli, B. Strengthening Cybersecurity Compliance: Evaluating Risk Assessment Frameworks and AI-Based Anomaly Detection in Audit Practices.
29. Kolla, S. H. (2022). Strategic Information Integration Models for Cross-Functional Service Optimization in Large-Scale Enterprises. International Journal of Emerging Trends in Engineering and Management Research, 7(3), 11811.
30. Yandamuri, U. S. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. International Journal Of Finance, 36(6), 682-706.
31. Davuluri, P. S. L. N. (2023). Integrating artificial intelligence into event-driven financial crime compliance platforms. International Journal of Finance, 36(6), 707-736.
32. Meda, R., & Pamisetty, A. (2023). Intelligent Infrastructure for Real-Time Inventory and Logistics in Retail Supply Chains. Educational Administration: Theory and Practice, 29(4), 5215-5233.
33. Mashetty, S. (2023). Leveraging Data Analytics to Enhance Affordable Housing Initiatives and Community Development. Available at SSRN 5249221.
34. Mattaparthi, R. (2023). Connected Fleet Intelligence: Edge-Centric Analytics and Computer Vision for Predictive Manufacturing and Asset Resilience. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(5), 9077-9088.
35. Aitha, A. R. (2021). Dev Ops Driven Digital Transformation: Accelerating Innovation In The Insurance Industry. Journal of International Crisis and Risk Communication Research.
36. Segireddy, A. R. (2021). Containerization and Microservices in Payment Systems: A Study of Kubernetes and Docker in Financial Applications. Universal Journal of Business and Management, 1(1), 1-17.
37. Mangala, N. (2022). Real-Time Data Quality Monitoring and Gating Frameworks in Cloud-Based Data Pipelines. International Journal of Research and Applied Innovations, 5(6), 8197-8219.
38. Peddi, R. K. (2021). Optimizing Case Management Workflows in Global Data Center Colocation Services. Universal Journal of Computer Sciences and Communications, 1(1), 1-21.
39. Loganathan, R. (2021). Integrated Risk and Compliance Frameworks for Global Data Center Operations: A Governance-Centric Approach. Universal Journal of Computer Sciences and Communications, 1(1), 1-26.
40. Amistapuram, K. (2021). Digital Transformation in Insurance: Migrating Enterprise Policy Systems to. NET Core. Universal Journal of Computer Sciences and Communications, 1(1), 1-17.
41. Reddy, V. A. R. (2023). Predictive Healthcare Administration Using Advanced Payer Analytics and Population Health Data Engineering. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(2), 7967-7978.
42. Gottimukkala, V. R. R. (2022). Licensing Innovation in the Financial Messaging Ecosystem: Business Models and Global Compliance Impact. International Journal of Scientific Research and Modern Technology, 1(12), 177-186.
43. Mangalampalli, B. M. (2022). Automated Invoice Validation Systems Using Advanced SQL Analytics in Healthcare Insurance. Front Health Inform, 11.
44. Reddy, V. A. R. (2021). Challenges in Standardizing Member Eligibility Data Across Multi-Payer Healthcare Ecosystems. International Journal of Medical Toxicology and Legal Medicine, 24(3), 1-19.
45. Davuluri, P. S. L. (2023). AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems (December 15, 2023).
46. Yandamuri, U. S. (2021). A Comparative Study of Traditional Reporting Systems versus Real-Time Analytics Dashboards in Enterprise Operations. Universal Journal of Business and Management, 1(1), 1-13.
47. Divya, V., & Bandi, V. K. (2023). Cloud-Native Model Lifecycle Management for Enterprise AI Systems. International Journal of Scientific Research and Modern Technology, 78.
48. Kolla, S. K., & Reddy, V. A. R. (2023). Deep Learning Architectures For Multimodal Medical Data Integration. South Eastern European Journal of Public Health, 248–260.
49. Bandi, V. D. V. K. (2023, November). Production-Grade Machine Learning Pipelines For Healthcare Predictive Analytics. South Eastern European Journal of Public Health, 189–205.
50. Kolla, S. H., & Loganathan, R. (2023). Cloud-Native Deep Learning Architectures For Secure Generative AI Deployment In Enterprise Workflow Platforms. Journal of International Crisis and Risk Communication Research, 603-618.
51. Mangala, N. (2021). Optimizing Large-Scale ETL Pipelines Using Medallion Architecture on Azure Data Lake. Journal of Artificial Intelligence and Big Data, 1(1), 1-20.
52. Paleti, S. (2023). AI-driven innovations in banking: Enhancing risk compliance through advanced data engineering. Available at SSRN, 5244840.
53. Reddy, V. A. R. (2023). Orchestrating the Future Autonomous Healthcare Data Pipeline Management through Agentic AI Architectures. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(2), 7979-7992.
54. Kolla, S. K. (2023). Learning Health Systems Machine Intelligence for Clinical Prediction and Healthcare Optimization. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(2), 7955-7966.
55. Valiki, D., & Segireddy, A. R. (2023). Deep Learning Architectures Deployed on Cloud Platforms for Dynamic Financial Risk Evaluation and Market Prediction. American International Journal of Computer Science and Technology, 5(5), 12-24.
56. Amistapuram, K. Energy-Efficient System Design for High-Volume Insurance Applications in Cloud-Native Environments. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), DOI, 10.
57. Kaulwar, P. K., Pamisetty, A., Mashetty, S., Adusupalli, B., & Pandiri, L. (2023). Harnessing intelligent systems and secure digital infrastructure for optimizing housing finance, risk mitigation, and enterprise supply networks. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 372-402.
58. DAVULURI, P. N. (2022). Cloud-Native Data Platform Modernization for Regulatory Compliance in Global Banking. Kurdish Studies.
59. Kolla, S. H. (2023). Large Language Model-Driven Enterprise Service Intelligence for Digital Workflow Transformation. International Journal of Research and Applied Innovations, 6(1), 8380-8391.
60. Loganathan, R. (2022). Converging Security Architecture and Compliance Management in Enterprise Data Center Ecosystems: A Unified Control Framework. International Journal of Scientific Research and Modern Technology, 1(12), 295-312.
61. Yandamuri, U. S. (2022). Cloud-Based Data Integration Architectures for Scalable Enterprise Analytics. International Journal of Intelligent Systems and Applications in Engineering, 10, 472-483.
62. Kolla, T., & Kolla, S. K. (2023). FHIR-Based Real-Time Healthcare Analytics using Unsupervised Learning. International Journal of Future Innovative Science and Technology (IJFIST), 6(6), 11751.
63. Mangala, N. (2022). Implementing Databricks Unity Catalog For Centralized Data Governance In Multi-Business-Unitenterprises. Journal of International Crisis and Risk Communication Research, 101-122.
64. Bandi, V. D. V. K. (2023). MLOps frameworks for reliable model deployment in cloud data platforms. Journal of Artificial Intelligence and Big Data, 3(1), 84-101.
65. Mangalampalli, B. M. (2021). Scalable Data Warehouse Architecture for Population Health Management and Predictive Analytics. World Journal of Clinical Medicine Research, 1(1), 1-18.
66. Mashetty, S. (2023). A Comparative Analysis of Patented Technologies Supporting Mortgage and Housing Finance. Available at SSRN 5249181.
67. Meda, R. (2023). Developing AI-Powered Virtual Color Consultation Tools for Retail and Professional Customers. Journal for ReAttach Therapy and Developmental Diversities. https://doi. org/10.53555/jrtdd. v6i10s (2), 3577.
68. Paleti, S. (2023). Trust layers: AI-augmented multi-layer risk compliance engines for next-gen banking infrastructure. Available at SSRN, 5221895.
69. Challa, K. (2023). Transforming Travel Benefits through Generative AI: A Machine Learning Perspective on Enhancing Personalized Consumer Experiences. Educational Administration: Theory and Practice. Green Publication. Educational Administration: Theory and Practice. Green Publication. https://doi. org/10.53555/kuey. v29i4, 9241.