The Institute for Ethical AI & Machine Learning @EthicalML
Research centre focused on developing Frameworks for the responsible design and deployment of AI. Led by volunteers: https://t.co/9F73u7BDq9ethical.institute London, EnglandJoined April 2018
Landed in London for #KubeCon Europe! If you are around let's catch up! Also join us tomorrow in our opening keynote at the KubeCon AI Day on the state of GenAI & ML in the Cloud Native Ecosystem 🚀
🥳🥳🥳 Happy New Year 2025 🥳🥳🥳 How time flies! We are celebrating 6 years since starting this weekly MLE newsletter 🎉🎉🎉
Check out the deep dives and resources in this week's edition: buff.ly/3U7DCrp
Insightful news, resources and frameworks in the Machine Learning Ecosystem this week: National AI Strategies & Regulation, Netflix's Large Scale Time Series Infra, Gauge on GenAI's Tech Debt Costs, Gradient Flow on AI Engineers, MLOps Organisational Setups + more 🚀
Check out…
Central functions support machine learning ambitions across organisations; only ~30% of organizations have a central Data / ML platform, and less than 10% have central AI Risk Governance:
Establishing central functions to support teams is a growing trend in machine learning…
In Machine Learning, Spreadsheets just keep coming back - this time as one of the top choices for experiment tracking and model registry tools... Only a week since we launched our survey on the State of Production ML we are already seeing great insights, help us map the broader…
Meta / Facebook has released their "approach to explaining (ML) ranking" 💡
This includes how posts are ranked in newsfeed, recommendations, ranking of comments, friend recommendations, notifications and more.
transparency.fb.com/features/expla…#ML#MachineLearning
New free online course on "Large Language Models: Application through Production" 💡
A great resource for developers, data scientists, and engineers who aim to build applications powered by large language models.
edx.org/course/large-l…#ML#MachineLearning
Dealing with Train-serve Skew in Real-time ML Models: A Short Guide 🔭
A comprehensive article that demystifies the training-serving skew on ML models, which arises due to differences between training and serving environments in real-time ML models
building.nubank.com.br/dealing-with-t…#ML
Stanford has put together an informative repo which introduces the "Foundation Model Transparency Index"; a framework which rates major AI providers like OpenAI and Google on their adherence to requirements specified in the EU Parliament's AI Act.
Repo: github.com/stanford-crfm/…
New course from Andrew Ng's DeepLearning.AI in collaboration with AWS on Generative AI 🚀
It includes data gathering, model selection, performance evaluation, deployment, and how to adapt and optimize models to various use cases.
deeplearning.ai/courses/genera…#ML#AI
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Global award-winning movement on a mission to empower young people across the world to create ethical #artificialintelligence
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