La plateforme d'observabilité de l'IA WhyLabs est une solution inter-cloud qui permet le MLOps en fournissant des fonctionnalités de suivi des modèles et des données. Elle prend en charge la surveillance de tout type de données à n'importe quelle échelle. La plateforme aide à détecter plus rapidement les problèmes de données et d'apprentissage automatique (ML), apporte des améliorations continues et prévient les incidents coûteux.
Pour utiliser la plateforme d'observabilité de l'IA WhyLabs, vous devez intégrer les agents spécialement conçus avec vos pipelines de données existants et vos architectures multi-cloud. La plateforme offre une intégration sécurisée avec des agents intégrés qui analysent les données brutes sans les déplacer ni les dupliquer, garantissant ainsi la confidentialité et la sécurité des données. Vous pouvez ensuite surveiller en continu vos modèles prédictifs, modèles génératifs, pipelines de données et magasins de fonctionnalités à l'aide des agents intégrés. La plateforme prend également en charge la surveillance de données structurées ou non structurées en exécutant whylogs sur vos données et en téléchargeant les journaux sur la plateforme.
Plus de contacts, visitez la page Contactez-nous(https://whylabs.ai/contact-us#form)
Plateforme d'observabilité de l'IA WhyLabs Nom de l'entreprise : WhyLabs, Inc. .
Pour en savoir plus sur Plateforme d'observabilité de l'IA WhyLabs, veuillez visiter la la page À propos de nous(https://whylabs.ai/about) .
Lien de connexion Plateforme d'observabilité de l'IA WhyLabs : https://hub.whylabsapp.com
Plateforme d'observabilité de l'IA WhyLabs Lien d'inscription : https://hub.whylabsapp.com/signup
Lien de tarification Plateforme d'observabilité de l'IA WhyLabs : https://whylabs.ai/pricing
Lien de Youtube Plateforme d'observabilité de l'IA WhyLabs : https://www.youtube.com/channel/UC9lRj988vfMTuUryc0JMPvA
Lien de Linkedin Plateforme d'observabilité de l'IA WhyLabs : https://www.linkedin.com/company/whylabsai
Lien de Twitter Plateforme d'observabilité de l'IA WhyLabs : https://twitter.com/whylabs
Lien de Github Plateforme d'observabilité de l'IA WhyLabs : https://github.com/whylabs
Par Lucy le Mai 14 2024
Débloquez le triomphe commercial : 14 stratégies cruciales de surveillance et de reporting !
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Intro to LLM Security - OWASP Top 10 for Large Language Models (LLMs)
Workshop links: WhyLabs Sign-up: https://whylabs.ai/free LangKit GitHub (give us a star!): https://github.com/whylabs/langkit Colab Notebook: https://bit.ly/whylabs-OWASPLLM10 Join the Responsible AI Slack Group: http://join.slack.whylabs.ai/ Join our workshop designed to equip you with the knowledge and skills to use LangKit with Hugging Face models. Guided by WhyLabs CEO Alessya Visnjic, you'll learn how to assess the security risks of your LLM application and how to protect your application from adversarial scenarios. This workshop will cover how to tackle the OWASP Top 10 security challenges for Large Language Model Applications (version 1.1). LLM01: Prompt Injection LLM02: Insecure Output Handling LLM03: Training Data Poisoning LLM04: Model Denial of Service LLM05: Supply Chain Vulnerabilities LLM06: Sensitive Information Disclosure LLM07: Insecure Plugin Design LLM08: Excessive Agency LLM09: Overreliance LLM10: Model Theft What you’ll need: A free WhyLabs account (https://whylabs.ai/free) A Google account (for saving a Google Colab) Who should attend: Anyone interested in building applications with LLMs, AI Observability, Model monitoring, MLOps, and DataOps! This workshop is designed to be approachable for most skill levels. Familiarity with machine learning and Python will be useful, but it's not required to attend. By the end of this workshop, you’ll be able to implement security techniques to your large language models (LLMs) . Bring your curiosity and your questions. By the end of the workshop, you'll leave with a new level of comfort and familiarity with LangKit and be ready to take your language model development and monitoring to the next level.
Alessya Visnjic, whylabs.ai | Supercloud 6
Alessya Visnjic, co-founder and CEO of WhyLabs, speaks with theCUBE Research analyst John Furrier during Supercloud 6, discussing the transformative impact of MLOps and generative AI on organizational strategies. They explore how these technologies are evolving the AI stack, making AI application creation more accessible to developers and emphasizing the need for enterprises to secure their data to expedite business decisions. Get insights into what you might have missed at Supercloud 6: AI Innovators https://siliconangle.com/2024/03/15/three-insights-ai-innovators-walmart-uber-supercloud6/ The conversation further delves into the rapid expansion and introduction of new products aimed at improving observability and control over applications. Visnjic highlights the significance of making AI-powered applications more accessible and the role of big cloud companies in simplifying deployment processes. Security challenges in LLMs and AI-gen applications are also discussed, focusing on new security metrics and strategies for safe adoption. Check out the full article https://siliconangle.com/2024/03/12/organizational-ai-strategies-mlops-generative-ai-2024-supercloud6/ Explore theCUBE's complete Supercloud event series coverage at supercloud.world https://events.cube365.net/supercloud Catch up on theCUBE's video coverage of Supercloud 6: AI Innovators https://www.youtube.com/watch?v=plEK22-zEJE&list=PLenh213llmcZKPngL8n5CHYDnr4weWfYB #theCUBE #Supercloud6 #theCUBEResearch #WhyLabs #MLOps #observability #security
Intro to ML Monitoring: Data Drift, Quality, Bias and Explainability
Workshop Links: - Free WhyLabs Signup: https://whylabs.ai/free - Notebook: https://bit.ly/ml-monitor-colab - whylogs github (give us a star!) https://github.com/whylabs/whylogs/ - Join The AI Slack group: https://bit.ly/r2ai-slack LLM monitoring: https://github.com/whylabs/langkit If you want to build reliable pipelines, trustworthy data, and responsible AI applications, you need to validate and monitor your data & ML models! In this workshop we’ll cover how to ensure model reliability and performance to implement your own AI observability solution from start to finish. Once completed you'll also receive a certificate! This workshop will cover: Detecting data drift Measuring model drift Monitoring model performance Data quality validation Measuring Bias & Fairness Model explainability What you’ll need: A modern web browser A Google account (for saving a Google Colab) Sign up free a free WhyLabs account (https://whylabs.ai/free) Who should attend: Anyone interested in AI Observability, Model monitoring, MLOps, and DataOps! This workshop is designed to be approachable for most skill levels. Familiarity with machine learning and Python will be useful, but it's not required. By the end of this workshop, you’ll be able to implement data and AI observability into your own pipelines (Kafka, Airflow, Flyte, etc) and ML applications to catch deviations and biases in data or ML model behavior. About the instructor: Sage Elliott enjoys breaking down the barrier to AI observability, talking to amazing people in the Robust & Responsible AI community, and teaching workshops on machine learning. Sage has worked in hardware and software engineering roles at various startups for over a decade. Connect with Sage on LinkedIn: https://www.linkedin.com/in/sageelliott/ About WhyLabs: WhyLabs.ai is an AI observability platform that prevents data & model performance degradation by allowing you to monitor your data and machine learning models in production. https://whylabs.ai/ Check out our open-source data & ML monitoring project: https://github.com/whylabs/whylogs Do you want to connect with the community, learn about WhyLabs, or get project support? Join the WhyLabs + Robust & Responsible AI community Slack: https://bit.ly/rsqrd-slack
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