- WEBINAR "Secure LLM App Deployments—Strategies and Tactics"Link visible for attendees
To access this webinar, please register here: https://hubs.ly/Q02xxRBT0
Topic: "Secure LLM App Deployments—Strategies and Tactics"
Speaker: Alexandre Landeau, Data Science Lead at Giskard
Alexandre collaborates closely with clients to understand their needs and have them secure their models against performance issues, cybersecurity threats, and ethical biases. Previously, he spent 5 years as a Data Scientist and Full-stack Software Engineer in several domains.Abstract:
In this session, you'll discover how to improve the security of Large Language Models (LLMs). We'll show you how to use red-teaming techniques from cybersecurity to identify and evaluate vulnerabilities in LLM applications, ensuring its safety and reliability.
Additionally, you'll learn how Giskard's tools can be integrated into your workflow for automatic vulnerability detection, allowing you to scale your security efforts for Generative AI.ODSC Links:
• Get free access to more talks/trainings like this at Ai+ Training platform:
https://hubs.li/H0Zycsf0
• ODSC blog: https://opendatascience.com/
• Facebook: https://www.facebook.com/OPENDATASCI
• Twitter: https://twitter.com/_ODSC & @odsc
• LinkedIn: https://www.linkedin.com/company/open-data-science
• Slack Channel: https://hubs.li/Q02zdcSk0
• Code of conduct: https://odsc.com/code-of-conduct/ - ODSC IN-PERSON MEETUP "Automating Data Curation for AI" - Hosted by CleanLab75 Hawthorne St, San Francisco, CA
Pre-registration is REQUIRED. It is important to RSVP on lu.ma here - https://hubs.li/Q02z0n3Y0
This meetup is co-organized by CleanLab and ODSC.
Who: People who build, develop and apply LLMs or wish to learn more about them
When: 5.30-8:30pm, Thursday June, 13 2024
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Speaker: Curtis Northcutt CEO, Co-Founder of CleanlabCurtis Northcutt is CEO and Co-Founder of Cleanlab, an AI software company that reduces the time and cost to improve machine learning model performance. He completed his PhD at MIT, where he invented Cleanlab’s algorithms for automatically finding and fixing label issues in any dataset. He was a recipient of MIT’s Morris Levin Thesis Award, an NSF Fellowship, and a Goldwater Scholarship and has worked at several leading AI research groups including Google, Oculus, Amazon, Facebook, Microsoft, and NASA.
Talk: Automating Data Curation for AI: Algorithms and theory for finding and improving mislabeled data in any machine learning dataset.
Summary:
The coupling of machine intelligence and human intelligence has the potential to empower humans with augmented capabilities (e.g., improving rhyme-density while writing song lyrics, enhancing empathy via emotion detection, and personalizing learning in online courses).Unfortunately, humans operate in an uncertain world – where the performance of even the most sophisticated model-centric artificially intelligent system often depends on its data-centric ability to deal with the uncertainty in the labels upon which it is trained.
To this end, we introduce confident learning whereby a machine (like humans) must learn with noisy-labeled data, directly quantify and identify label noise, and unlearn misconceptions by re-learning with confidence on cleaned data with erroneous labels removed. We achieve this by developing a principled theory and framework for confident learning with affordances for quantifying, identifying, and learning with label errors in data, and we open-source their implementations in the cleanlab Python package.
Based on human verification of the label errors found using cleanlab: we estimate a 3.4% lower bound error rate of the test set labels of ten of the most commonly used machine learning datasets across audio, image, and text modalities; examine the noise prevalence needed to change machine benchmark rankings; and provide corrected test sets so that humans can
benchmark machine performance with increased confidence.We'll conclude the talk with several real-world customer use cases of Cleanlab Studio, a SaaS version of the open-source package, built on top of confident learning and other related algorithmic approaches.
ODSC Links:
• Get free access to more talks/trainings like this at Ai+ Training platform:
https://hubs.li/H0Zycsf0
• ODSC blog: https://opendatascience.com/
• Facebook: https://www.facebook.com/OPENDATASCI
• Twitter: https://twitter.com/_ODSC & @odsc
• LinkedIn: https://www.linkedin.com/company/open-data-science
• Slack Channel: https://hubs.li/Q02w1GKB0
• Code of conduct: https://odsc.com/code-of-conduct/ - Live Training "Introduction to AI Agents"Link visible for attendees
This course is free with any paid subscription to Ai+ Training Platform - https://hubs.li/H0Zycsf0
This course will equip you with an understanding of the fundamentals AI agents and their capabilities. you’ll explore what AI agents are, how they work, and their real-world applications through hands-on notebooks to build practical, real-world skills. If you are a novice to AI agents or seeking a better understanding of how this technology will impact your work, this is the course for you.
Instructor's bio: Sheamus McGovern, Founder and Engineer | ODSC
Sheamus McGovern is the founder of ODSC (The Open Data Science Conference). He is also a software architect, data engineer, and AI expert. He started his career in finance by building stock and bond trading systems and risk assessment platforms and has worked for numerous financial institutions and quant hedge funds. Over the last decade, Sheamus has consulted with dozens of companies and startups to build leading-edge data-driven applications in finance, healthcare, eCommerce, and venture capital. He holds degrees from Northeastern University, Boston University, Harvard University, and a CQF in Quantitative Finance.
ODSC Links:
• Get free access to more talks/trainings like this at Ai+ Training platform:
https://hubs.li/H0Zycsf0
• ODSC blog: https://opendatascience.com/
• Facebook: https://www.facebook.com/OPENDATASCI
• Twitter: https://twitter.com/_ODSC & @odsc
• LinkedIn: https://www.linkedin.com/company/open-data-science
• Slack Channel: https://hubs.li/Q02zdcSk0
• Code of conduct: https://odsc.com/code-of-conduct/