I just wanted to share the win here. I've been quiet here lately, but that's because I've been neck deep studying the Microsoft Azure learning path for the AI-102 exam. I took the exam today and passed it. Link to the exam and study materials if anyone else is interested: https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-engineer/?practice-assessment-type=certification P.S. I've used Azure before for 2-3 years and considered myself proficient in it, but AI is new and it felt like learning Azure all over again. Very useful skill to have, imho.

Just wanted to share the win I've been wanting to get for several months... I got a new job in the data science / ML field, where the domain matches my masters degree. I start today. If it weren't for this group and what I learned from it, I might not have made it through the interview process (4 rounds plus a take-home assignment). I also want to encourage others to keep at it, not give up, even when things seem darkest. Success may just be around the corner!

Does anyone use AI to generate README files for their GitHub? If so, please list them here!

Is there a good tutorial that gives you hands on experience with PyTorch? Thanks in advance!

When starting out your data career it's hard to know what skills to focus on. There are many blog posts offering recommendations, but none of which are backed by data. "Isn't that exactly what a data professional should do?" thought Luke Barousse. So he made datanerd.tech, a data platform giving real-time insight into what the most in-demand data skills are. One downside of the platform was that it aggregated all data, so you were unable to see how skill importance changed over time. After writing a bit of Python and SQL I made a Github pull request doing exactly that. The feature is now live, so feel free to have a look.