AI catalyst conference building commercially successful LLM applications

You are a data scientist, software developer, ML engineer or other technical professional who would like to be able incorporate profitable state-of-the-art LLMs into real-world applications. You're an entrepreneur, intrapreneur or other professional who's keen to accelerate commercial inno...

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Bibliographic Details
Format: eBook
Published: [Place of publication not identified] Pearson 2023
Edition:[First edition]
Online Access:
Collection: O'Reilly - Collection details see MPG.ReNa
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300 |a 1 video file (2 hr., 50 min.)  |b sound, color 
505 0 |a Jon Krohn: Welcome -- Vin Vashishta: Framing Profitable Commercial Uses of LLMs -- Caterina Constantinescu: LLM Selection and Implementation -- Krishnaram Kenthapadi: Deploying Trustworthy LLMs -- Jon Krohn: Closing Remarks 
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653 |a Generative programming (Computer science) / Congresses 
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520 |a You are a data scientist, software developer, ML engineer or other technical professional who would like to be able incorporate profitable state-of-the-art LLMs into real-world applications. You're an entrepreneur, intrapreneur or other professional who's keen to accelerate commercial innovation and profitability at your firm. Prerequisites All you need is an interest in how AI can impact you and your organization. Feel free to bring questions for the experts.  
520 |a In this talk, Vin will explain the roadmap for cost savings and revenue growth for the rest of us. Specifically, Vin will detail his four keys to how Generative AI will deliver for businesses: Generative AI monetization strategy; opportunity discovery and use case selection; Generative AI product design; and enterprise data management practices that curate high-value data sets. Vin Vashishta is the author of "From Data To Profit" (Wiley, 2023), the playbook for monetizing data and AI. He's also the Founder of V-Squared, one of the world's oldest data and AI consulting firms. His background combines over 25 years in strategy, leadership, software engineering and applied machine learning. Over 200,000 people follow Vin on social media for his valuable daily insights.  
520 |a 3 hours of video What you'll learn: Unlock profits and monetize generative AI with insights from industry leaders. Master the details of LLM selection, benchmarking, and the powerful Langchain library. Explore ethical AI and real-world solutions to address ethical concerns in generative AI. In an era in which artificial intelligence is revolutionizing industries, this event is a pivotal opportunity for professionals to stay ahead of the curve. The half-day conference cuts through the noise, delivering critical know-how on optimizing profits, technical implementation, and ethical considerations in the rapidly evolving landscape of Large Language Models (LLMs) and Generative AI. Whether you're a developer, data scientist or entrepreneur, this event offers an invaluable blend of expertise and actionable strategies to help you navigate the complexities and opportunities in commercial AI applications.  
520 |a Jon and Vin Discussion + Q&A (15 min) Caterina Constantinescu: LLM Selection and Implementation (30 minutes) Dive deep into the critical steps of developing a commercially viable Large Language Model (LLM) application. We will cover approaches for selecting the most appropriate LLM (be it open-source or proprietary) and then progress to a hands-on demonstration of how to fine-tune an LLM and thus ensure optimal user experience. We also introduce Langchain, a flexible and extensive Python library for LLM orchestration and integration within complex applications. By the end of this talk, you'll be well-positioned to transform foundational knowledge into actionable strategies for the next wave of NLP applications. Caterina Constantinescu is a Principal Data Consultant at GlobalLogic (part of the Hitachi Group) where she advises on data design and observability, as well as the tailored applicability of data science models to business use cases.  
520 |a You'll come away empowered to accelerate the deployment of profitable Generative AI models. AI Catalyst Conference The AI Catalyst Conference from Pearson brings together fresh voices in AI to make complex topics understandable and actionable. Host Jon Krohn guides the conversation and explains how to bring state-of-the-art methods into practice. Gain new information or a different perspective to make an impact in your job and in the world. What you'll learn and how you can apply it Monetization: Grasp the strategies to make AI and LLMs profitable. Technical Criteria: Know how to evaluate LLMs for your specific needs. Ethical Concerns: Understand the social impact of AI deployments. And you'll be able to: Implement Langchain: Utilize the new Python library for seamless LLM integration. Benchmark Models: Conduct performance tests to ensure optimal LLM functionality. Build Trust: Apply open-source tools to manage ethical concerns in AI. This course is for you because...  
520 |a Focusing on real-world LLM use cases, this talk details open-source solutions for tackling these concerns, enabling ML practitioners to build more reliable and trustworthy generative AI applications. Krishnaram Kenthapadi is the Chief AI Officer & Chief Scientist at Fiddler AI, a startup focusing on responsible AI and ML monitoring. Before Fiddler, he was a Principal Scientist at Amazon Web Services AI, leading initiatives in fairness, explainability, and privacy. He also led similar projects at LinkedIn AI and was part of Microsoft's AI and Ethics Advisory Board. Krishnaram completed his Ph.D. in Computer Science from Stanford University in 2006. He serves on the senior program committees of major conferences, has published 50+ papers with 4500+ citations, and holds 150+ patent filings (70 granted).  
520 |a Recommended Follow-up Attend: Large Language Models in 3 Weeks by Sinan Ozdemir Attend: Optimizing Large Language Models by Shaan Khosla Read: Quick Start Guide to Large Language Models by Sinan Ozdemir Watch: Quick Guide to ChatGPT, Embeddings and Other Large Language Models by Sinan Ozdemir Read: Designing Machine Learning Systems by Chip Huyen For technical deep-dives, read David Foster's Generative Deep Learning, 2nd Edition and Lewis Tunstall et al.'s Natural Language Processing with Transformers, Revised Edition For a more general introduction to deep learning, check out the Deep Learning: The Complete Guide playlist by Dr. Jon Krohn: Course Schedule Jon Krohn: Welcome (10 minutes) Vin Vashishta: Framing Profitable Commercial Uses of LLMs (30 minutes) Nvidia, Microsoft, and Google have charted paths to profitability with Generative AI.  
520 |a Throughout her career, she has applied data science methods across various industries such as e-retail, healthcare, finance, ESG and more, while also having organized the R meetup in Edinburgh for several years. Caterina holds a PhD awarded by the University of Edinburgh. Jon and Caterina Discussion + Q&A (15 min) Break 10 min Krishnaram Kenthapadi: Deploying Trustworthy LLMs (30 minutes) Generative AI models and applications are being rapidly deployed across industries, but there are critical ethical and social considerations. These concerns include lack of interpretability, bias and discrimination, privacy, lack of model robustness, fake and misleading content, copyright implications, plagiarism, and the environmental impact associated with training and inference of generative AI models.  
520 |a Jon and Krishnaram Discussion + Q&A (15 min) Jon Krohn: Closing Remarks (10 minutes) About the Host: Jon Krohn is Co-Founder and Chief Data Scientist at the machine learning company Nebula. He authored the book Deep Learning Illustrated, an instant #1 bestseller that was translated into seven languages. He is also the host of SuperDataScience, the data science industry's most listened-to podcast. Jon is renowned for his compelling lectures, which he offers at leading universities and conferences, as well as via his award-winning YouTube channel. He holds a PhD from Oxford and has been publishing on machine learning in prominent academic journals since 2010