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Sunila Gollapudi

Academia Engagements

I am a visiting faculty at ISB and teach Data and Knowledge Graph related modules for AMPBA Program. I mentor capstone projects in Vision, and Advanced AI.

Course: Data Engineering

This module aims to provide a comprehensive understanding of Data Engineering, equipping class with both theoretical knowledge and practical skills. Participants will learn to design, implement, and manage large-scale data-driven applications using the latest tools and technologies, preparing them to handle real-world data challenges effectively. The Modern Data architectures and strategy today are solving a different class of data requirements that are pivoting

  • Foundational Knowledge: Grasp essential Data Engineering concepts and the role of Data Engineering within the broader data ecosystem.

  • Data Classification & Management: Understand various types of enterprise data and their classifications based on purpose, sensitivity, and compliance requirements.

  • Database & Storage Solutions: Differentiate between SQL and NoSQL databases, explore modern data storage architectures, and implement Delta Tables.

  • Data Integration & Orchestration: Master data ingestion patterns, transformation techniques, and orchestration tools like Apache NiFi and Airflow.

  • Data Governance & Security: Learn best practices for data quality management, security, and compliance with data privacy regulations.

  • Practical Skills: Gain hands-on experience through labs and advanced projects, designing data layers for real-world applications such as Netflix, Uber, WhatsApp, and BookMyShow.

This Data Engineering module offers an in-depth exploration of the field, starting with foundational concepts and progressing to advanced topics and hands-on applications. The curriculum covers essential areas such as data classification, database architectures, data storage solutions, integration pipelines, orchestration techniques, and data governance. Through a blend of theoretical lessons and practical labs, students will engage with industry-standard tools and technologies, preparing them to design and manage robust data infrastructures and applications. The module culminates in advanced projects where participants will design data layers for prominent enterprises, applying their learned skills to solve complex data engineering challenge

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Course: Enterprise Knowledge Graphs & Applied AI

This course on Enterprise Knowledge Graphs and Advanced AI offers a comprehensive dive into the world of graphs and their transformative applications in modern enterprises. Starting with the fundamentals, participants will explore graph theory, algorithms, and programming paradigms to understand the power of graphs in representing complex relationships. The session delves into the evolution of the Semantic Web, covering RDF, RDFS, OWL, and SPARQL, and provides practical experience in building ontologies using tools like Grafo, with real-world examples such as FIBO or BioSNAP Ontology. Transitioning to enterprise contexts, the course highlights the role of knowledge graphs in creating a knowledge layer, managing vocabularies, and generating personalized insights. Participants will gain hands-on experience with leading tools like Stardog and Neo4j to represent and query knowledge effectively.

Building on this foundation, advanced sessions explore techniques for extracting knowledge from unstructured data using NLP and graph embeddings addressing tasks like entity extraction, semantic role labeling, and knowledge discovery. The course also examines architectural best practices and tools like OpenIE for practical implementation. Moving to cutting-edge integrations, participants will learn to apply generative AI esp LLMs to knowledge graphs, mastering contextual inferencing, embeddings-driven content generation, and explainable AI techniques.

Tools like Neo4j, LangChain, and LlamaIndex will be used to implement these concepts, ensuring practical understanding.The final session focuses on real-world use cases, showcasing applications in enterprise data fabrics, digital twins, fraud analytics, and healthcare. Participants will design scalable architectures and gain insights into emerging trends, research directions, and practical challenges. By the end, learners will have a robust understanding of knowledge graphs, their integration with advanced AI, and the skills to create impactful solutions for complex enterprise problems. The hands-on approach ensures immediate applicability in industry settings, making this course a comprehensive guide for leveraging knowledge graphs and AI technologies.

  1. Foundational Knowledge of Graphs and Semantic Web

    1. Gain a comprehensive understanding of graph theory, algorithms, and databases, enabling participants to represent and analyze complex relationships effectively.

    2. Learn the evolution and practical implications of the Semantic Web, alongside standards like RDF, RDFS, and OWL for semantic data modeling.

  2. Deep into Enterprise Knowledge Graphs

    1. Develop skills in creating and managing enterprise knowledge graphs, including defining the knowledge layer, developing enterprise vocabularies, and applying inferencing and reasoning techniques.

    2. Understand the importance of contextual and personalized insights, data provenance, and virtualization within enterprise ecosystems.

  3. Hands-On learning with Industry-Leading Tools

    1. Apply concepts through hands-on sessions with tools like Stardog, Neo4j, LangChain, OpenIE, and LlamaIndex, gaining practical experience in building and querying knowledge graphs.

    2. Create ontologies, explore graph architectures, and implement knowledge graph workflows for real-world use cases.

  4. Advanced AI Techniques in Knowledge Graphs

    1. Learn advanced methods for integrating AI and generative AI into knowledge graphs, including contextual inferencing, embeddings-driven content generation, and explainable AI.

    2. Explore techniques for extracting, representing, and reasoning over unstructured data in enterprise contexts.

  5. Real-World Applications and Future Directions

    1. Understand the diverse applications of knowledge graphs in domains such as enterprise data fabric, fraud detection, and healthcare, while exploring emerging trends and research areas.

    2. Leave equipped to design scalable, future-proof knowledge graph solutions that align with modern enterprise needs.

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©2021 by Sunila Gollapudi. 

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