RKRakesh Kumar AgrawalEnterprise AI · Platform · Cloud

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RKRakesh Kumar AgrawalEnterprise AI · Platform · Cloud

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Cloud Computing

Exploring cloud-native architectures for scalable AI delivery, observability, and data-intensive workloads.

Future work

Expand reference workload patterns for secure multi-cloud AI systems.
Publish cloud observability baselines for model serving reliability.

cloudscalabilityobservabilityarchitecture

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Related Publications

Digital Brain for IT Operations and Observability: An AI-Augmented Cognitive Framework for Incident Intelligence
2026 | International Journal on Science and Technology | DOI 10.71097/ijsat.v17.i2.10738
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BQEB ForecastBench: Benchmarking AI Models for Smart Grid Forecasting Using BQEB-Data v1
2026 | Zenodo public work | DOI 10.5281/zenodo.19716383
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Related GitHub Repositories

RakeshKumarAgrawal.github.io
Personal portfolio repository showcasing enterprise AI research, platform engineering, cloud architecture, open-source projects, and technical publications.
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Rakesh-K-Agrawal
Public repository profile visible on GitHub.
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Related Datasets

BQEB-Data BIO-Quantum Energy Brain Benchmark Dataset for Smart Grid Intelligence, Renewable Forecasting, Storage Optimization, and Cyber Resilience
2026 | Zenodo | DOI 10.5281/zenodo.19657256
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BQEB-Data: An Open Benchmark Dataset for Autonomous Energy Intelligence and Smart Grid Analytics
2026 | Zenodo | DOI 10.5281/zenodo.19656915
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Related Frameworks

BQEB-Data
Open benchmark dataset for autonomous energy intelligence and smart grid analytics.
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BQEB ForecastBench
Benchmarking AI models for smart grid forecasting using BQEB-Data v1.
View source

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Let's discuss research collaborations, enterprise AI strategy, platform engineering, open science initiatives, or speaking opportunities.