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Last updated : Apr 24, 2019
Not on the current edition
This blip is not on the current edition of the Radar. If it was on one of the last few editions it is likely that it is still relevant. If the blip is older it might no longer be relevant and our assessment might be different today. Unfortunately, we simply don't have the bandwidth to continuously review blips from previous editions of the Radar Understand more
Apr 2019
Adopt ? We feel strongly that the industry should be adopting these items. We use them when appropriate on our projects.

Over the past couple of years, we've noticed a steady rise in the popularity of analytics notebooks. These are Mathematica-inspired applications that combine text, visualization and code in a living, computational document. Jupyter Notebooks are widely used by our teams for prototyping and exploration in analytics and machine learning. We've moved Jupyter to Adopt for this issue of the Radar to reflect that it has emerged as the current default for Python notebooks. However, we caution to use Jupyter Notebooks in production.

May 2018
Trial ? Worth pursuing. It is important to understand how to build up this capability. Enterprises should try this technology on a project that can handle the risk.

Over the last couple of years, we've noticed a steady rise in the popularity of analytics notebooks. These are Mathematica-inspired applications that combine text, visualization and code in a living, computational document. Increased interest in machine learning — along with the emergence of Python as the programming language of choice for practitioners in this field — has focused particular attention on Python notebooks, of which Jupyter seems to be gaining the most traction among ThoughtWorks teams. People seem to keep finding creative uses for Jupyter beyond a simple analytics tool. For example, see Jupyter for automated testing.

Nov 2017
Assess ? Worth exploring with the goal of understanding how it will affect your enterprise.

Over the last couple of years, we've noticed a steady rise in the popularity of analytics notebooks. These are Mathematica-inspired applications that combine text, visualization and code in a living, computational document. In a previous edition, we mentioned GorillaREPL, a Clojure variant of these. But increased interest in machine learning — along with the emergence of Python as the programming language of choice for practitioners in this field — has focused particular attention on Python notebooks, of which Jupyter seems to be gaining the most traction among Thoughtworks teams.

Veröffentlicht : Nov 30, 2017
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