The niche coding language for machine learning jobs


We wrote on Julia, the coding language that claims to be a hybrid between Python and C ++, a few times on this site before, but not in a while – and certainly not since it was used by Pfizer and AstraZeneca develop new drug treatments.

Co-created in 2009 by Viral Shah, a PhD from UC Santa Barbara, Julia has been slowly gaining followers for a decade. Its main selling point is that it eliminates the need for a two-step process of testing, modeling, and prototyping in a high-level language such as Python, and then rewriting in a faster second lower-level language such as than C ++. Julia is the holy grail: she can do anything.

“Iyou have the ease of use of Python but the performance of C ++, “Shah boasts of her creation, adding that Julia is not just Easy to learn (this should take about a week if you are a Python coder), but incredibly fast. In that sense, it’s ideal for financial services users – many of whom are proficient Python coders, but need the speed to run trading algorithms or assess the creditworthiness of a large mortgage portfolio.

Julia was not designed with financial services in mind alone, but finance developers are one of the main Julia communities, making up about 7% of Julia’s users, Shah explains. Constantin Gonciulea, the distinguished engineer from JPMorgan who recently joined Wells Fargo is a fan. Speakers at this month JuliaCon, Julia’s annual user conference, features an engineer from BlackRock Labs.

BlackRock is Julia’s best-known user in finance – the language is used to power parts of her famous Aladdin tech platform, originally an internal risk tool that is now widely used in the industry. Julia is also used by State Street for FX trading and by what Shah says, many hedge funds and trading companies do not want their names made public.

Some in finance are reluctant to use Julia too liberally due to her relatively low uptake. Indeed, the Tiobe index suggests Julia still has a long way to go before it’s anything close to the mainstream – she ranks 35th with just 0.35% of users this month, far behind Rust with 0.49%, but Shah says Tiobe is “very sensitive” to small changes in usage. “Julia is already one of the top 20 languages,” he says. “Over the next decade he will reach the top 10.”

Julia’s expansion will be aided by the fact that Julia Computing, a company founded by Shah that provides scalable enterprise computing solutions built around Julia, just received $ 24 million in Series A fundraising and added former Snowflake CEO Bob Muglia to its board. The money will be used to grow the Julia ecosystem and to scale a cloud platform that allows its developers to build and deploy Julia programs and models. It will also serve to double Julia’s current workforce of 40, most of whom work remotely. Avik Sengupta, Julia’s senior vice president of engineering, previously worked for financial service providers and could hire to fit her. “We need people who can develop the solutions that will be relevant to our finance clients,” Shah says – financial history will be taken into account.

However, Julia’s developers don’t just work in finance. As Muglia points out, the language is highly suited to the world of digital models and machine learning and, as such, can be used in applications on smartphones, advanced materials, pharmaceuticals and aeronautics. Julia already has 6,000 libraries, Shah says, and she can also use any libraries created for R, Python, and C ++.

“Scientists and data engineers are using products designed decades ago,” says Shah. “- JuliaHub makes it possible to design new drugs and therapies, develop new batteries, simulate a space mission and map the universe, all while using less computing resources and reducing emissions from data centers.” Tests Return coding algorithms and analysis of large portfolios can also be added to this list.

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photo by Viktor Forgacs at Unsplash

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