Foundation Models for Big Data and Prompt Tuning

Foundation models are expansive AI systems capable of addressing diverse challenges across multiple domains. These models play a critical role in driving innovation and achieving business objectives. However, their success heavily depends on robust big data management throughout the entire lifecycle, particularly during pre-training stages, with a focus on data acquisition, preparation, and storage. This paper examines the key challenges and best practices for managing big data in the context of foundation models, emphasizing architectures that enable fine-tuning and prompt-tuning, where scalable and efficient data pipelines are crucial. The discussion highlights the importance of prompt engineering, including the creation and maintenance of prompt libraries, as a critical area for enhancing model accuracy and adaptability. Operational challenges such as resource management, data security, and scalability are explored, along with actionable recommendations to ensure reliable performance in collaborative, multi-party settings. These insights aim to improve the generalization, robustness, and usability of foundation models.
Who is this presentation for?
Data Engineer,Architect,AI/ML enthusiast
 

An innovative hands-on technology leader with an analytic approach, and 16+ years delivering Enterprise-wide Data Warehouse, Business Intelligence, Data Engineering,Artificial Intelligence, Gen AI, Data Analytics and Big data modernization solutions for various US clients. He has extensive experience on Data architecture, Enterprise architecture , Microservice architecture, Cloud Computing and Data Governance and Data Quality.Leverage cutting-edge technologies (cloud, big data) and data science expertise (MS in DataScience. UIUC) to architect, build, and migrate data platforms.He has architected and led the team to design and build automated data pipelines on cloud to move the data between different zones raw, stage and warehouse. He also involved in developing, and managing high throughput low latency applications for both hosted and deployed systems.Lead cross-functional teams (25+) for seamless data ingestion, curation, transformation, and Data product development on cloud marketplaces.Proven ability in distributed infrastructure and on-prem to cloud migration.