R-Brain Blog
A Case Study of Text Analytics in AllenNLP, Google, AWS, Azure and IBM Watson
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Recently, I was introduced to Allen Institute for AI and was impressed by AllenNLP. This Natural Language Processing (NLP) project is an open source deep learning toolkit with a set of pre-trained core models and applications mainly for NLP such as Semantic Role Labeling, Natural Entity Recognition (NER), and Textual Entailment. In this article, I review this solution and compare the performance of its NER model with text analytics APIs in Google Cloud, Amazon AWS, Microsoft Azure and IBM Watson.
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RIDE Server - A Multi-User and Scalable Data Science IDE Server
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Data Science has become the default for practically every industry, from banking to transportation to communication to retail. But there's one fundamental space out of which it has yet to be addressed. Ironically, model development and data analysis - the process of editing, building, debugging and analyzing code - is still primarily done in different frameworks or even on localhost systems. Organizations are moving toward server-based integrated development environments (IDEs) due to the tremendous benefits of these systems. However, when it comes to data science, they face significant challenges for which RIDE Server is a perfect solution.
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RIDE - A New Data Science IDE for Python and R
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The data science world is split into two parts: the (i)Python and the R community. Both groups offer a plethora of tools and libraries enriching our work-life as a data scientist. Interestingly, many of the offerings are complementary, such that professional data scientists should know both environments to pick the right tool for the job. In many cases, it even makes sense to use Python and R together in the same project. Sadly, today these two worlds don’t integrate very well, so we need to switch back and forth between different tools and environments.
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Introducing R-Brain: A New Data Science Platform
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R-Brain is a next generation platform for data science built on top of Jupyterlab with Docker. It was recently unveiled at JupyterCon in late August. Don’t let the name fool you. R-Brain currently supports R, Python, Structured Query Language (SQL), and more. It has integrated intellisense, debugging, packaging, and publishing capabilities. This cool new solution also has analytics workspace collaboration and marketplace features for personal, professional and enterprise use cases.
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Visual debugging with RIDE
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On R-Blogger there was an interesting article that compares debugging support for R in RStudio and Eclipse StatET [1]. I liked that article very much, but it misses the new RIDE environment, which I am going to add to the comparison in this article.
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