Anindita Mahapatra

Anindita Mahapatra

Solutions Architect at Databricks

    Anindita is a Lead Solutions Architect at Databricks in the Financial Services vertical focused on helping organizations make the most of their data investments. She has co-authored 2 patents and has over 20 years of industry experience in software development, consulting, and client-facing roles. Notably, she has authored the book "Simplifying Data Engineering and Analytics with Delta," a definitive guide to crafting analytics-ready data that powers artificial intelligence. She has a Masters in Computer Science from Boston University, a Masters in Liberal Arts and Management from Harvard Extension School. Anindita's commitment to knowledge dissemination is evident through her current role as a graduate course instructor on Data Engineering at Harvard Extension School.

    All Sessions by Anindita Mahapatra

    Day 2 04/24/2024
    3:30 pm - 4:00 pm

    Data Engineering in the Era of Gen AI

    <span class="etn-schedule-location"> <span class="firstfocus">Data Engineering</span> </span>

    In the era of Gen AI, the landscape of data engineering is undergoing a transformative evolution, and this talk delves into the pivotal role it plays in harnessing the power of artificial intelligence. The session explores the dynamic interplay between data engineering and the emerging generation of AI technologies, highlighting key strategies to adapt and thrive in this data-driven era. The discussion begins by examining the unique challenges and opportunities posed by Gen AI, where advanced machine learning algorithms and neural networks demand a sophisticated and scalable data infrastructure. The speaker emphasizes the importance of building resilient pipelines that can seamlessly integrate diverse and massive datasets, ensuring a robust foundation for training and deploying AI models. The talk also delves into the crucial aspect of data quality and governance in the context of Gen AI, emphasizing the need for meticulous data engineering practices to mitigate biases and ensure ethical AI development. Furthermore, the session explores cutting-edge technologies and best practices, such as real-time data processing and federated learning, that empower data engineers to stay at the forefront of innovation. Ultimately, this talk serves as a comprehensive guide for data engineers navigating the complexities of Gen AI, offering insights, strategies, and real-world examples to inspire and equip professionals in the rapidly evolving field of data engineering.

    Open Data Science

     

     

     

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