Googlielmo's blog

 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...

 Jupyter notebooks allows LaTeX rendering inside markdown. This way you can write complex math equations within a notebook. While LaTeX is the de facto standard for scientific documents, it hasn't a very friendly and intuitive syntax. handcalcs is an Open Source library for converting Python calculations into rendered LaTeX: just write the symbolic formula, followed by numeric substitutions and that's it. After install it (it is available through PyPI), in the simplest case you just need to import the render class and use the %%render magic command to render the content of a cell: Here another example of equation render and numeric substitution: It is also possible to render just the symbolic equation: or any way generate the corresponding LaTeX code: By default handcalcs renders code vertically, but it is possible to use the %%render params magic to save space by rendering in a single line or show just the result of a calculation: handcalcs allows to adjust precision, use Gr...

  Faker is an Open Source Python package that generates synthetic data that could be used for many things such as populating a database, do load testing or anonymize production data for development or ML purposes. Generating fully random data isn't a good choice: with Faker you can drive the generation process and tailor the generated data to your specific needs: this is the greatest value provided by Faker. This package comes with 23 built-in data providers, some other providers are available from the community. The available data providers cover majority of data types and cases, but it is possible any way make the generated data more meaningful by implementing a custom provider. Faker supports Python 3.6+ and it is available for installation through PyPI or Anaconda.  Here's a code example that shows how to implement a custom provider to generate synthetic data following the structure and constraints as for this Kaggle  dataset related to a restaurant data with consumer...

 In my career I have noticed that often organizations are reluctant on providing Engineering teams with licenses for software to draw diagrams. In the best case scenarios MS Visio is usually the only option available, which isn't the best experience when trying to draw modern software architectures. Several online options are available, but they require to share project details that cannot leave your organization network, so they couldn't be taken into account often. Also, while treating everything as code, it would be nice to have also diagrams as code. All these needs can be satisfied by adopting Diagrams . It is an Open Source Python package that allows you draw cloud system architecture diagrams programmatically and then put them under version control, (as at the end they are regular Python files). It supports cloud (AWS, Azure, GCP, Alibaba, Oracle) and on-prem system architecture diagrams. The Diagrams nodes include also Kubernetes, programming languages and frameworks. ...

 Photo by Dinu J Nair on Unsplash Today I want to introduce  TagUI , an RPA (Robotic Process Automation) Open Source tool I am using to automate test scenarios for web applications. It is developed and maintained by the AI Singapore national programme. It allows writing flows to automate repetitive tasks, such as regression testing of web applications. Flows are written in natural language : English and other 20 languages are currently supported. Works on Windows, Linux and macOS. The TagUI official documentation can be found  here . The tool doesn't require installation: just go the official GitHub repository and download the archive for your specific OS (ZIP for Windows, tar.gz for Linux or macOS). After the download is completed, unpack its content in the local hard drive. The executable to use is named  tagui  (.cmd in Windows, .sh for other OS) and it is located into the  <destination_folder>/tagui/src  directory. In order to ...

I am a strong supporter of Open Source since a long time. I understand that the Open Souce movement is important to promote the knowledge sharing in a very wide audience, that it has helped (and it's still helping) to speed up the progress of the IT world and that it brought benefits in terms of quality to the commercial products too. But there is a plea I'd like to do to all the Open Source project holders: please add some documentation to your project. A lot of Open Source projects still suffer of the following lacks: Missing or poor documentation. In the last months I found some potential helpful libraries or plugins (most part of them hosted on GitHub) for the projects I was working on, but no documentation at all or just a short description of the project and nothing else. I know I could browse the source code (I like to do so and add some contribution or send some feedback to help improving something if possible), but at the end I spent less time to implement a solutio...

JPPF ( http://www.jppf.org/ ) is a Java framework which can help to implement and run a parallel Java program. It allows a Java application to run independent parts of it on different machines in parallel in order to dramatically reduce their processing time. It is useful to split a computing-intensive Java application into several mostly independent parts (which are typically called tasks). JPPF is Open Source and released under the Apache license 2.0. These are the main features of this framework: Allows a grid (a server, to which any number of execution nodes are attached.) to be up and running in minutes. Provides a simple programming model that abstracts the complexity of distributed and parallel processing . It's an highly scalable (from small to large networks) and distributed framework for the parallel execution of cpu intensive tasks. Provides seamless integration with the most popular J2EE application servers (IBM WebSphere, JBoss, WebLogic, etc.).   ...