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10-time award winner in Artificial Intelligence and Open Source and the co-author of the book 'Sculpting Data For ML', Jigyasa Grover is a powerhouse brimming with passion for making a dent in this world of technology and bridging the gender gap. AI & Research Lead, she has many years of ML engineering & Data Science experience in deploying large‐scale low-latency systems for user personalization and monetization on popular social networking apps like Twitter and Facebook, and e‐commerce at Faire, particularly ads prediction, sponsored content ranking, and recommendation with a recent focus on Generative AI. She is also one of the few ML Google Developer Experts and Google Women Techmaker Ambassadors globally. As a World Economic Forum’s Global Shaper, she ensures the leverage of her technical skills and connections for solution-building, policy-making, and lasting change.
Recently, she was featured at the Google I/O 2024 main keynote and introduced by Sundar Pichai on the stage as she spoke about my experience using Gemini 1.5 Pro’s 1 million token context window. As a part of the Google Developer Advisory Board (gDAB) she collaborates with experts and contributes to shaping the future of Google's developer ecosystem. She is also involved with Google Developer Experts and Google for Startups, and most recently was on the Generative AI panel with some renowned folks from the industry and startup world.
Jigyasa Grover
Award Winning AI Engineer & Leader
English
Languages:

Location:
San Francisco , USA

Can also give an online talk/webinar
Paid only. Contact speaker for pricing!
MY TALKS
Guarding the LLM Galaxy: Security, Privacy, and Guardrails in the AI Era
Data / AI / ML, Software Engineering, Women in Tech, Diversity and Inclusion, Innovation, Inspirational, Security / Privacy, Entrepreneurship, Leadership, Business Development, Professional Development, Soft Skills, General, Content, Design, Product



The widespread adoption of Large Language Models (LLMs) like GPT-4, Claude, and Gemini has introduced unprecedented capabilities and equally unprecedented risks. Organizations are increasingly deploying LLMs to handle sensitive tasks, from processing medical records to analyzing financial documents. This talk examines the evolving landscape of LLM security and privacy, combining theoretical foundations with a walkthrough of example implementations.
Through real-world case studies of both attacks and defenses and practical implementation guidance using popular security tools, we'll explore critical vulnerabilities and proven defensive techniques. Special attention will be given to securing fine-tuned and domain-specific LLMs, with live examples using NVIDIA’s NeMo Guardrails, LangChain's security tools, and Microsoft's guidance library.

Sculpting Data for Machine Learning: Generative AI edition
General, Data / AI / ML, Software Engineering, Soft Skills, Entrepreneurship, Leadership, Business Development, Diversity and Inclusion, Content, Marketing, Innovation, Inspirational, Women in Tech, Security / Privacy, Design, Product, Professional Development



The emergence of GenAI has revolutionized various domains, from creative content generation with text, synthetic images, video and so much more. However, the success and effectiveness of GenAI models heavily rely on the quality of the underlying data during the fine-tuning process. Volumes of crude data are available on the web nowadays; all we need are the skills to identify and extract meaningful datasets and present them to GenAI models to unleash their full potential. This talk presents the power of the most fundamental aspect of AI - Data Curation, which often does not get its due limelight. It will also walk the audience through constructing good-quality datasets with hands-on Pythonic examples. By emphasizing the indispensability of quality data, this talk underscores the need for robust data collection and preprocessing practices to propel the advancements in GenAI.

Guarding the LLM Galaxy: Security, Privacy, and Guardrails in the AI Era
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Sculpting Data for Machine Learning: Generative AI edition
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