Shaurya Agrawal
Start-up CTO; Board Advisor at Hoonartek
Austin, Texas, United States
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Shaurya Agrawal is a Data & Analytics leader with 25+ years of experience driving transformative initiatives across Tech/SaaS, E-commerce, and FinTech. With expertise in AI/ML, Enterprise Data Architecture, and BI, he's led impactful projects, creating customer-centric solutions and modernizing data platforms. As a CTO of YourNxt Technologies, a mobile tech start-up and Board Advisor to Hoonartek, Shaurya shapes global data strategies.
Holding an MBA and pursuing an MS in Data Science from UT Austin, Shaurya leverages data to unlock business value, specializing in unified customer views and personalized experiences.
Area of Expertise
Topics
Operationalizing ML Governance: A Practical Guide to MLOps in Microsoft Fabric
This session will focus on the practical implementation of MLOps within Microsoft Fabric to achieve effective AI/ML model governance. We'll walk through the end-to-end process of building automated pipelines for model training, deployment, monitoring, and retraining, emphasizing how Fabric's integrated tools (Synapse, Data Factory, MLflow) facilitate version control, experiment tracking, and continuous validation. We'll also discuss strategies for seamless integration with Power BI for operational reporting and real-time insights into model performance and governance metrics.
Integrating Microsoft Fabric and Databricks for Modern Data Analytics
This session will explore the strategic integration of Microsoft Fabric and Databricks to build a robust Cloud centric modern data analytics ecosystem. We will delve into how these powerful platforms complement each other, leveraging Microsoft Fabric's comprehensive suite for data integration, governance, and business intelligence with Databricks' advanced capabilities for data engineering, AI/ML, and complex analytics at scale.
Attendees will learn best practices for seamless data flow, optimizing performance, and unlocking deeper insights by combining the strengths of both environments. We will be drawing on real-world examples from FinTech and E-commerce to illustrate practical implementation strategies and benefits for enterprise data architecture.
Getting Started with Vector Search on Databricks: Building Intelligent Search Applications
Vector search is a hot topic in the AI/ML and data engineering space, especially with the rise of generative AI and semantic search. Databricks recently introduced native vector search capabilities, making it a timely for discussion
End-to-End Machine Learning Pipelines on Databricks: From Data Ingestion to Model Deployment
See the full lifecycle of ML on Databricks, including data engineering, feature engineering, model training, and deployment.
Disrupting Defaults: Smarter Credit Risk w/ Neo4j Graphs
In this lightning talk, Shaurya Agrawal will demonstrate how Neo4j’s graph database technology can revolutionize credit risk management for Financial and FinTech firms by uncovering hidden relationships between entities. Using a real-world scenario where Company A, Company B, and Company C, are subsidiaries, with varying ownership, under a common parent, you will see how traditional systems often miss these indirect connections—potentially underestimating aggregate exposure and risk. With Neo4j, you will learn how to model and visualize complex corporate hierarchies, instantly revealing cross-entity dependencies and shared liabilities.
Attendees will discover how graph queries can surface risk concentrations, identify circular ownership, and support more informed credit decisions. This session will show you how leveraging Neo4j’s relationship-first approach enables smarter, faster risk assessment—empowering you to move beyond the limitations of legacy, table-based systems.
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