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November 28 · Issue #32 · View online
Curated essays about the future of Data Science. Production Data Science and learning resources for continuous learning. Covers Data Science, Data Engineering, MLOps & DataOps. Curated by people at https://nibble.ai/
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Are we still in the steam-powered days for machine learning?
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Applying DevOps principles to AI and ML
A Gartner report about facing challenges to move advanced analytics into production and how and where DevOps best practices can help. Among the key challenges that organizations face with operationalizing ML are security/privacy concerns, complexity in integrating AI workloads with their core infrastructure, building an automation pipeline and ensuring cohesive collaboration between data science teams and IT.
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We're still in the steam-powered days of machine learning
ML platforms are complicated, unique, and, so far, hard to reproduce. There really is no good, generalized single system of best practices for creating machine learning platforms. There’s not even a textbook on it.
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AI adoption proves its worth, but few scale impact
Most companies report measurable benefits from AI where it has been deployed; however, much work remains to scale impact, manage risks, and retrain the workforce. AI high performers tend to engage in value-capturing practices. […] These include, among others, aligning business, analytics, and IT leaders on the potential value at stake from AI across each business domain; investing in talent, such as translator expertise; and ensuring that business staff and technical teams have the skills necessary for successful scaling.
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How to recognize AI snake oil
Much of what’s being sold as “AI” today is snake oil — it does not and cannot work. Why is this happening? How can we recognize flawed AI claims and push back?
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Why scientists need to be better at data visualization
The scientific literature is riddled with bad charts and graphs, leading to misunderstanding and worse. Data visualization can be essential for analyzing data, communicating experimental results and even for making surprising discoveries. Yet, scientists receive very little visualization training. This article reviews some of the principles that can drive good data visualization.
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Lyft recently open-sourced Flyte
Flyte is an open source, K8s-native extensible orchestration engine that manages the core machine learning pipelines at Lyft: ETAs, pricing, incentives, mapping, vision, and more.
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PyTorch makes Deep Learning with PyTorch book available for free
To help developers get started with PyTorch, PyTorch is making the pdf version of the “Deep Learning with PyTorch” book, written by Luca Antiga and Eli Stevens, available for free to the community for a limited time.
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Hacking Neural Networks: A Short Introduction
A small course on exploiting and defending neural networks. A very hands-on approach with coding exercises.
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Gaussian Processes, not quite for dummies
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