Data science is at the heart of many of the innovative solutions that we see on the market. Yet, when it comes to the struggles of professionals who are working in this area, setting up an environment has been constant. They often spend more time on preparing it, than actually exploring new ways to get data insight. But, why is this a pain?
😥 𝐓𝐨𝐨𝐥𝐬 𝐟𝐫𝐚𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐜𝐨𝐧𝐟𝐢𝐠𝐮𝐫𝐚𝐭𝐢𝐨𝐧:This is common in the whole industry and the quick development of new libraries, frameworks and tools does not make it easier. To that, we also need to add the time spent on actually configuring different parts of the stack, such as the GPUs.
😥 𝐃𝐫𝐢𝐯𝐞𝐫𝐬 𝐢𝐧𝐜𝐨𝐦𝐩𝐚𝐭𝐢𝐛𝐢𝐥𝐢𝐭𝐲: The versioning dependency is often a nightmare for data scientists and data engineers, especially when they are not so experienced. Every update or upgrade is basically a risk of breaking the entire ML environment.
😥 𝐒𝐞𝐭𝐮𝐩 𝐭𝐢𝐦𝐞: It often takes much longer, mostly because of the abundance of tools to choose from, possible incompatibilities as well as additional configurations that need to be done.
😥 𝐂𝐨𝐦𝐩𝐮𝐭𝐞 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬: Whereas having powerful hardware is needed to run AI at scale, for anyone who does initial exploration, having an AI workstation, ideally with a GPU should be enough.
Canonical's mission to innovate at speed with open source AI has determined us to put together Data Science Stack, a developer tool that helps you set up your ML environment with ease and within minutes. Our objective is to see if we address the challenges of data scientists and data engineers out there, so together with Lidia Luna Puerta, we are conducting user research to gather feedback about our beta release.
We won't take much of your time and you will have your ML environment set up very quickly. Just sign up here: https://lnkd.in/dV2MBxHC
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