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Wednesday September 3, 2025 5:00pm - 5:50pm PDT
Shailaja Sampat, Fujitsu Research of America, Senior Researcher

- Are you tired of manually processing large datasets in spreadsheets?

- Have you considered using Machine Learning (ML) to automate your tasks but feel limited by your coding skills or time constraints?

- Looking for ways to reduce the time spent in making your data AI-ready, despite your ML background?

If these challenges sound familiar, this session is for you!

Traditionally, building predictive models has required specialized coding and statistical expertise. Moreover, data professionals often dedicate over 80% of their time to the labor-intensive data-wrangling process- to prepare raw data for consumption by automated machine learning (AutoML) tools. To overcome these hurdles, we introduce AutoDW, an innovative data wrangler that utilizes the power of Large Language Models (LLMs) and sophisticated automation to facilitate the seamless preparation of AI-ready data. This session will demonstrate how the synergistic application of AutoDW and AutoML empowers users to rapidly develop predictive prototypes for their business use cases end-to-end without writing any code. Through step-by-step instruction and a live demonstration, attendees will witness AutoDW's intelligent data processing, observe AutoML's autonomous algorithm selection tailored to specific applications, and, most importantly, gain a comprehensive understanding of how to interpret the resulting predictive outputs.

#AutoML #DataWrangling #NoCodeML #FastMLPrototyping #DataDrivenDevelopment
Speakers
avatar for Shailaja Sampat

Shailaja Sampat

Senior Researcher, Fujitsu Research of America
Shailaja Sampat is a senior researcher in the AI lab at Fujitsu's research division in the USA. She earned her Ph.D. from Arizona State University, with a thesis focusing on the intersection of natural language processing and computer vision. Her current research spans autoML, data... Read More →
Wednesday September 3, 2025 5:00pm - 5:50pm PDT
DataWeek -- Main Stage

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