Packaging Python has been a painful experience for long. The history of the various distribution that Python offered along the years is really bumpy, and both the user and developer experience has been pretty bad. Fortunately, things improved a lot in the recent years, with the reconciliation of setuptools and distribute. Though in the context of the OpenStack project, a solution on top of setuptools has been already started a while back. Its usage is now spread across a whole range of software and libraries. This project is called pbr, for Python Build Reasonableness. Don’t be afraid by the OpenStack colored themed of the documentation – it is a bad habit of OpenStack folks to not advertise their tooling in an agnostic fashion. The tool has no dependency with the cloud platform, and can be used painlessly with any package.
If there are any doubts in regards to the popularity of Keras among the Data Scientist/Engineer community and the mindshare it commands, you just need to look at the support it has been receiving from all major AI and Cloud players. Currently the official Keras release already supports Google’s TensorFlow and Microsoft’s CNTK deep learning libraries besides supporting other popular libraries like Theano. Last year Amazon Web Services announced its support for Apache MXNet, another powerful Deep Learning library and few weeks ago support for Keras was added to the MXNet’s next release candidate. As of now MXNet only seems to support Keras v1.2.2 and not the current Keras release 2.0.5.
Along the way, we have learned a thing or two about how to get started in AI, how to build momentum in AI and ultimately how to succeed with AI. We share these with customers and prospects, but wanted to be sure we did so more broadly.
While health care and financial services leap to mind as industries most in need of data governance processes and frameworks, the fact is an ever-increasing number and variety of organizations are facing the challenge of managing their data assets in a hyper-responsible manner to ensure not only data integrity and security, but, increasingly, data privacy.
If you are a newbie in the world of machine learning, then this tutorial is exactly what you need in order to introduce yourself to this exciting new part of the data science world. This post includes a full machine learning project that will guide you step by step to create a “template,” which you can use later on other datasets.
Microsoft R Open (MRO), Microsoft’s enhanced distribution of open source R, has been upgraded to version 3.4.1 and is now available for download for Windows, Mac, and Linux. This update upgrades the R language engine to R 3.4.1 and updates the bundled packages. MRO is 100% compatible with all R packages. MRO 3.4.1 points to a fixed CRAN snapshot from September 1 2017, and you can see some highlights of new packages released since MRO 3.4.0 on the Spotlights page. As always you can use the built-in checkpoint package to access packages from an earlier date (for compatibility) or a later date (to access new and updated packages). MRO 3.4.1 is based on R 3.4.1, a minor update to the R engine (you can see the detailed list of updates to R here. If you’ve had problems installing packages on Windows, this update does fix a bug that affected some users. It’s also backwards-compatible with R 3.4.0 (and MRO 3.4.0), so you shouldn’t encounter an new issues by upgrading. Also note that 3.4.2 is also around the corner (MRO 3.4.2 will be released in October). We hope you find Microsoft R Open useful, and if you have any comments or questions please visit the Microsoft R Open forum. You can follow the development of Microsoft R Open at the MRO Github repository. To download Microsoft R Open, simply follow the link below.