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ML Olympiad returns with over 20 challenges

AI News

The popular ML Olympiad is back for its third round with over 20 community-hosted machine learning competitions on Kaggle. Over the previous two rounds, an impressive 605 teams participated across 32 competitions, generating 105 discussions and 170 notebooks. Caridá (AI/ML GDE) involves classifying toxic tweets.

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Your guide to generative AI and ML at AWS re:Invent 2024

AWS Machine Learning Blog

This year, generative AI and machine learning (ML) will again be in focus, with exciting keynote announcements and a variety of sessions showcasing insights from AWS experts, customer stories, and hands-on experiences with AWS services. Fifth, we’ll showcase various generative AI use cases across industries.

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Accelerate AWS Well-Architected reviews with Generative AI

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In this post, we explore a generative AI solution leveraging Amazon Bedrock to streamline the WAFR process. We demonstrate how to harness the power of LLMs to build an intelligent, scalable system that analyzes architecture documents and generates insightful recommendations based on AWS Well-Architected best practices.

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Secure a generative AI assistant with OWASP Top 10 mitigation

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A common use case with generative AI that we usually see customers evaluate for a production use case is a generative AI-powered assistant. If there are security risks that cant be clearly identified, then they cant be addressed, and that can halt the production deployment of the generative AI application.

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Improving the Accuracy of Generative AI Systems: A Structured Approach

Speaker: Anindo Banerjea, CTO at Civio & Tony Karrer, CTO at Aggregage

💥 Anindo Banerjea is here to showcase his significant experience building AI/ML SaaS applications as he walks us through the current problems his company, Civio, is solving. The number of use cases/corner cases that the system is expected to handle essentially explodes.

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Build, Deploy, and Manage ML Models with Google Vertex AI

Analytics Vidhya

Vertex AI is a unified platform from Google Cloud offering tools and infrastructure to build, deploy, and manage machine learning models.

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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

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This post is part of an ongoing series about governing the machine learning (ML) lifecycle at scale. The data mesh architecture aims to increase the return on investments in data teams, processes, and technology, ultimately driving business value through innovative analytics and ML projects across the enterprise.

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