Recently I have read this article (http://tinyurl.com/d3lnxns) and the related comments on ComputerWeekly.com and then I asked myself (for the billionth time):"Why the hell in 2013 are there still people frightened by Open Source?". Some governments and big organizations are discovering belatedly the advantages of Open Source software adoption, after wasting a lot of money on license purchasing and above all on maintenance, bug fixing, support and customization (wasting a lot of time too (and time is more precious than money)) . It's sad to see that in some cases the choice of Open Source is just because someone thinks that it's cheaper than proprietary: Open Source means also the possibility to reduce the learning curve of a software, to quickly discover and fix possible bugs, to find a way to improve or extend the code (no lock-ins as for the proprietary), to have a large community to share tips and suggestions, to improve your personal knowledge and to find often a better quality. I am no dogmatic about Open Source: if a proprietary solution is well coded and stable, suits fine my business requirements and grants me a real, efficient and prompt support I have no problems to adopt it. But each time there is a good Open Source alternative I prefer this one for the reasons above. Furthermore I think that companies should decide to reinvest in people a big part of the money saved by adopting Open Source solutions: people are the real value of a company.
I just realized that I am using Streamlit since almost one year now, posted about in Twitter or LinkedIn several times, but never wrote a blog post about it before. Communication in Data Science and Machine Learning is the key. Being able to showcase work in progress and share results with the business makes the difference. Verbal and non-verbal communication skills are important. Having some tool that could support you in this kind of conversation with a mixed audience that couldn't have a technical background or would like to hear in terms of results and business value would be of great help. I found that Streamlit fits well this scenario. Streamlit is an Open Source (Apache License 2.0) Python framework that turns data or ML scripts into shareable web apps in minutes (no kidding). Python only: no front‑end experience required. To start with Streamlit, just install it through pip (it is available in Anaconda too): pip install streamlit and you are ready to execute the working de...
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