Streaming Feature Stores and Real-Time ML Inference on Cloud-Native Infrastructure
Keywords:
Streaming feature store, real-time machine learning, online inferenceAbstract
In the present day's fast-changing digital world, actual time machine learning (ML) is essential for making smart, responsive apps like fraud detection, recommendation systems, dynamic pricing & personalized user experiences. The feature store is the most important part of this change. It makes sure that ML models get the information that is timely, consistent & of high quality. This study investigates the evolution of feature stores from traditional batch pipelines to modern streaming architectures that provide real-time inference. As businesses increasingly embrace low-latency decision-making, the need for streaming feature storage has surged, facilitating the rapid production, modification, and transmission of features within milliseconds. We look at how cloud-native technology, such as Kubernetes, serverless platforms, and event-driven architectures, makes it possible to build real-time machine learning systems that can handle a lot of data, are reliable, and don't cost a lot of money. We want to provide a complete picture of how streaming feature stores function in practice, the architectural frameworks that support them, and the trade-offs that come with them. We demonstrate, via a practical case study, the use of a cloud-native stack to develop a real-time machine learning pipeline capable of ingesting and transforming substantial streaming data for low-latency inference. The case study clarifies the challenges faced, the implemented solutions, and the key performance outcomes. This article offers insights & practical guidance for data & ML engineers seeking to improve their machine learning workflows with streaming their capabilities, illustrating how the amalgamation of streaming data processing, feature engineering & cloud-native principles can enable the creation of sophisticated, actual time applications.
Downloads
References
Gupta, Abhishek, and Yashovardhan Chaturvedi. "Cloud-native ML: Architecting AI solutions for cloud-first infrastructures." Nanotechnology Perceptions 20 (2024): 930-939.
Mohammad, Abdul Jabbar. “Sentiment-Driven Scheduling Optimizer”. International Journal of Emerging Research in Engineering and Technology, vol. 1, no. 2, June 2020, pp. 50-59
Mishra, Sarbaree, et al. “Training AI Models on Sensitive Data - The Federated Learning Approach”. International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 1, no. 2, June 2020, pp. 33-42
Lakarasu, Phanish. "Designing Cloud-Native AI Infrastructure: A Framework for High-Performance, Fault-Tolerant, and Compliant Machine Learning Pipelines." Fault-Tolerant, and Compliant Machine Learning Pipelines (December 11, 2023) (2023).
Manda, Jeevan Kumar. "Zero Trust Architecture in Telecom: Implementing Zero Trust Architecture Principles to Enhance Network Security and Mitigate Insider Threats in Telecom Operations." Journal of Innovative Technologies 5.1 (2022).
Balkishan Arugula. “Order Management Optimization in B2B and B2C Ecommerce: Best Practices and Case Studies”. Artificial Intelligence, Machine Learning, and Autonomous Systems, vol. 8, June 2024, pp. 43-71
Olufemi, Omoniyi David, et al. "AI-enhanced predictive maintenance systems for critical infrastructure: Cloud-native architectures approach." World Journal of Advanced Engineering Technology and Sciences 13.02 (2024): 229-257.
Patel, Piyushkumar. "The Role of Central Bank Digital Currencies (CBDCs) in Corporate Financial Strategies and Reporting." Journal of Artificial Intelligence Research and Applications 3.2 (2023): 1194-1.
Deng, Shuiguang, et al. "Cloud-native computing: A survey from the perspective of services." Proceedings of the IEEE 112.1 (2024): 12-46.
Balkishan Arugula. “AI-Driven Fraud Detection in Digital Banking: Architecture, Implementation, and Results”. European Journal of Quantum Computing and Intelligent Agents, vol. 7, Jan. 2023, pp. 13-41
Shaik, Babulal, and Jayaram Immaneni. "Enhanced Logging and Monitoring With Custom Metrics in Kubernetes." African Journal of Artificial Intelligence and Sustainable Development 1 (2021): 307-30.
Datla, Lalith Sriram. “Optimizing REST API Reliability in Cloud-Based Insurance Platforms for Education and Healthcare Clients”. International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 4, no. 3, Oct. 2023, pp. 50-59
Jonnakuti, Srikanth. "Graph Neural Networks for Entity Resolution in Cloud-Native Data Platforms." (2022).
Mohammad, Abdul Jabbar. “Sentiment-Driven Scheduling Optimizer”. International Journal of Emerging Research in Engineering and Technology, vol. 1, no. 2, June 2020, pp. 50-59
Mishra, Sarbaree. “Building a Chatbot for the Enterprise Using Transformer Models and Self-Attention Mechanisms”. International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 2, no. 2, June 2021, pp. 72-82
Mohammad, Abdul Jabbar. “Dynamic Labor Forecasting via Real-Time Timekeeping Stream”. International Journal of AI, BigData, Computational and Management Studies, vol. 4, no. 4, Dec. 2023, pp. 56-65
Guntupalli, Bhavitha. “ETL Architecture Patterns: Hub-and-Spoke, Lambda, and More”. International Journal of AI, BigData, Computational and Management Studies, vol. 4, no. 3, Oct. 2023, pp. 61-71
SAMUEL, AKINNIYI. "Cloud-Native AI solutions for predictive maintenance in the energy sector: A security perspective." Available at SSRN 5290068 (2021).
Jani, Parth, and Sangeeta Anand. "Compliance-Aware AI Adjudication Using LLMs in Claims Engines (Delta Lake+ LangChain)." International Journal of Artificial Intelligence, Data Science, and Machine Learning 5.2 (2024): 37-46.
Kumar Tarra, Vasanta, and Arun Kumar Mittapelly. “AI-Driven Lead Scoring in Salesforce: Using Machine Learning Models to Prioritize High-Value Leads and Optimize Conversion Rates”. International Journal of Emerging Trends in Computer Science and Information Technology, vol. 5, no. 2, June 2024, pp. 63-72
Allam, Hitesh. "Declarative Operations: GitOps in Large-Scale Production Systems." International Journal of Emerging Trends in Computer Science and Information Technology 4.2 (2023): 68-77.
Lakarasu, Phanish. "End-to-end Cloud-scale Data Platforms for Real-time AI Insights." Available at SSRN 5267338 (2022).
Abdul Jabbar Mohammad. “Integrating Timekeeping With Mental Health and Burnout Detection Systems”. Artificial Intelligence, Machine Learning, and Autonomous Systems, vol. 8, Mar. 2024, pp. 72-97
Manda, J. K. "IoT Security Frameworks for Telecom Operators: Designing Robust Security Frameworks to Protect IoT Devices and Networks in Telecom Environments." Innovative Computer Sciences Journal 7.1 (2021).
Jani, Parth, and Sarbaree Mishra. "Governing Data Mesh in HIPAA-Compliant Multi-Tenant Architectures." International Journal of Emerging Research in Engineering and Technology 3.1 (2022): 42-50.
Rahman, Mushfiq, et al. "CLOUD-NATIVE DATA ARCHITECTURES FOR MACHINE LEARNING." (2019).
Patel, Piyushkumar, and Deepu Jose. "Green Tax Incentives and Their Accounting Implications: The Rise of Sustainable Finance." Journal of Artificial Intelligence Research and Applications 4.1 (2024): 627-48.
Shaik, Babulal. "Automating Zero-Downtime Deployments in Kubernetes on Amazon EKS." Journal of AI-Assisted Scientific Discovery 1.2 (2021): 355-77.
Balkishan Arugula. “Cloud Migration Strategies for Financial Institutions: Lessons from Africa, Asia, and North America”. Los Angeles Journal of Intelligent Systems and Pattern Recognition, vol. 4, Mar. 2024, pp. 277-01
Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2024). Post-quantum cryptography: Preparing for a new era of data encryption. MZ Computing Journal, 5(2), 012077.
Lu, Yao, et al. "Computing in the era of large generative models: From cloud-native to AI-native." arXiv preprint arXiv:2401.12230 (2024).
Chaganti, Krishna Chaitanya. "Threat Modeling and Vulnerability Management for Securing IoT Ecosystems." International Journal of Emerging Trends in Computer Science and Information Technology (2025): 28-35.
Jani, Parth. "Document-Level AI Validation for Prior Authorization Using Iceberg+ Vision Models." International Journal of AI, BigData, Computational and Management Studies 5.4 (2024): 41-50.
Guntupalli, Bhavitha. “Data Lake Vs. Data Warehouse: Choosing the Right Architecture”. International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 4, no. 4, Dec. 2023, pp. 54-64
Nookala, G. (2023). Real-Time Data Integration in Traditional Data Warehouses: A Comparative Analysis. Journal of Computational Innovation, 3(1).
Mishra, Sarbaree. “The Lifelong Learner - Designing AI Models That Continuously Learn and Adapt To New Datasets”. International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 5, no. 1, Mar. 2024, pp. 68-78
Sikha, Vijay Kartik. "Cloud-Native Application Development for AI-Conducive Architectures."
Shaik, Babulal, Jayaram Immaneni, and K. Allam. "Unified Monitoring for Hybrid EKS and On-Premises Kubernetes Clusters." Journal of Artificial Intelligence Research and Applications 4.1 (2024): 649-669.
Abdul Jabbar Mohammad. “Leveraging Timekeeping Data for Risk Reward Optimization in Workforce Strategy.” Los Angeles Journal of Intelligent Systems and Pattern Recognition, vol. 4, Mar. 2024, pp. 302-24
Chaganti, Krishna Chaitanya. "AI-Powered Patch Management: Reducing Vulnerabilities in Operating Systems." International Journal of Science And Engineering 10.3 (2024): 89-97.
Allam, Hitesh. “Unifying Operations: SRE and DevOps Collaboration for Global Cloud Deployments”. International Journal of Emerging Research in Engineering and Technology, vol. 4, no. 1, Mar. 2023, pp. 89-98
Nguyen, Hieu. "Machine Learning Model Management using Cloud-Native Technologies for IoT." (2021).
Datla, Lalith Sriram. “Proactive Application Monitoring for Insurance Platforms: How AppDynamics Improved Our Response Times”. International Journal of Emerging Research in Engineering and Technology, vol. 4, no. 1, Mar. 2023, pp. 54-65
Manda, J. K. "Data privacy and GDPR compliance in telecom: ensuring compliance with data privacy regulations like GDPR in telecom data handling and customer information management." MZ Comput J 3.1 (2022).
Mishra, Sarbaree, and Jeevan Manda. “Improving Real-Time Analytics through the Internet of Things and Data Processing at the Network Edge ”. International Journal of Emerging Research in Engineering and Technology, vol. 5, no. 2, June 2024, pp. 41-51
Guntupalli, Bhavitha, and Surya Vamshi ch. “Designing Microservices That Handle High-Volume Data Loads”. International Journal of AI, BigData, Computational and Management Studies, vol. 4, no. 4, Dec. 2023, pp. 76-87
Xiong, Jinjun, and Huamin Chen. "Challenges for building a cloud native scalable and trustable multi-tenant AIoT platform." Proceedings of the 39th international conference on computer-aided design. 2020.
Patel, Piyushkumar. "AI and Machine Learning in Tax Strategy: Predictive Analytics for Corporate Tax Optimization." African Journal of Artificial Intelligence and Sustainable Development 4.1 (2024): 439-57.
Chaganti, Krishna Chiatanya. "Securing Enterprise Java Applications: A Comprehensive Approach." International Journal of Science And Engineering 10.2 (2024): 18-27.
Talakola, Swetha, and Sai Prasad Veluru. “Managing Authentication in REST Assured OAuth, JWT and More”. International Journal of Emerging Trends in Computer Science and Information Technology, vol. 4, no. 4, Dec. 2023, pp. 66-75
Immaneni, J. (2021). Scaling Machine Learning in Fintech with Kubernetes. International Journal of Digital Innovation, 2(1).
Rúa Martínez, Javier de la. Scalable architecture for automating machine learning model monitoring. Diss. ETSI_Informatica, 2020
Nookala, G. (2024). Adaptive data governance frameworks for data-driven digital transformations. Journal of Computational Innovation, 4(1).
Allam, Hitesh. "Security-Driven Pipelines: Embedding DevSecOps into CI/CD Workflows." International Journal of Emerging Trends in Computer Science and Information Technology 3.1 (2022): 86-97.
Datla, Lalith Sriram. “Postmortem Culture in Practice: What Production Incidents Taught Us about Reliability in Insurance Tech”. International Journal of Emerging Research in Engineering and Technology, vol. 3, no. 3, Oct. 2022, pp. 40-49
Joshi, Suyash, Basit Hasan, and R. Brindha. "Optimal declarative orchestration of full lifecycle of machine learning models for cloud native." 2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC). IEEE, 2024.
Veluru, Sai Prasad. "Self-Penalizing Neural Networks: Built-in Regularization Through Internal Confidence Feedback." International Journal of Emerging Trends in Computer Science and Information Technology 4.3 (2023): 41-49.
Mishra, Sarbaree, and Jeevan Manda. “Building a Scalable Enterprise Scale Data Mesh With Apache Snowflake and Iceberg”. International Journal of Emerging Research in Engineering and Technology, vol. 4, no. 2, June 2023, pp. 95-105
ElKenawy, Ahmed Sobhy. "An Enhanced Cloud-Native Deep Learning Pipeline for the Classification of Network Traffic." (2023).
