REDMOND, Wash., Feb. 6 — Microsoft has added JuliaPro to Windows Data Science Virtual Machine (DSVM), making it available on Microsoft Azure. Julia is now available for the first time on the two largest cloud environments, following the December 2016 launch of Julia on Amazon Web Services.

According to Viral Shah, CEO of Julia Computing, “We are thrilled to partner with Microsoft to make JuliaPro available to Microsoft Azure users via Windows Data Science Virtual Machine (DSVM). Now Julia users in finance, engineering, manufacturing, biomedical research and other areas of data science and scientific computing can access JuliaPro in both of the top two public cloud computing environments: Amazon Web Services and Microsoft Azure.”

This latest version of JuliaPro launched in December 2016, and includes the Julia Compiler, Debugger, Profiler, Juno Integrated Development Environment, more than 100 curated packages, data visualization and plotting. Integration with Excel, customer support and indemnity are available with JuliaPro Enterprise and JuliaFin. JuliaFin also includes Bloomberg integration, advanced time series analytics and Miletus, a custom Julia package for developing and executing complex trading strategies.

About Julia Computing and Julia

Julia Computing was founded in 2015 by the co-creators of the Julia language to provide support to businesses and researchers who use Julia.

Julia is the fastest modern high performance open source computing language for data and analytics. It combines the functionality and ease of use of R, Python, Matlab, SAS and Stata with the speed of Java and C++. Julia delivers dramatic improvements in simplicity, speed, capacity and productivity.

1. Julia is lightning fast.  Julia provides speed improvements up to 1,000x for insurance model estimation, 225x for parallel supercomputing image analysis and 11x for macroeconomic modeling.
2. Julia is easy to learn.  Julia’s flexible syntax is familiar and comfortable for users of Python and R.
3. Julia integrates well with existing code and platforms.  Users of Python, R and other languages can easily integrate their existing code into Julia.
4. Elegant code.  Julia was built from the ground up for mathematical, scientific and statistical computing, and has advanced libraries that make coding simple and fast, and dramatically reduce the number of lines of code required – in some cases, by 90% or more.
5. Julia solves the two language problem.  Because Julia combines the ease of use and familiar syntax of Python and R with the speed of C, C++ or Java, programmers no longer need to estimate models in one language and reproduce them in a faster production language.  This saves time and reduces error and cost.

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Source: Julia Computing