SOBRE ESTE EPISÓDIO
Matthew Renze is a data science consultant, author, and public speaker. He is the founder of Renze Consulting, an AI consulting company that has trained over 400,000 software developers and IT professionals. His clients range from small tech start-ups to Fortune 500 companies. He is also the President of Serenze Global, a 501(c)(3) non-profit organization dedicated to improving access to technology education for under-represented individuals by empowering the next generation of tech community leaders. Matthew is currently working on his Master's degree in Artificial Intelligence with a Data Science specialization at Johns Hopkins University. He currently has double degrees in Computer Science and Philosophy with a minor in Economics from Iowa State University. He is a Microsoft MVP in AI, an ASPInsider, and an author for Pluralsight, Udemy, and Skillshare. His interests include AI, ML, data science, mindfulness, technology education, and tech community leadership.
Topics of Discussion:
[3:37] How Matthew got into software development and rebranded himself as a data science consultant before going independent as a consultant. Now, he is in the process of rebranding as an AI consultant, rather than a data science consultant, still with a foundation in data science.
[4:41] What exactly is AI?
[6:23] Matthew discusses what a traveling salesman is.
[9:15] Matthew sorts out the difference between AI and ML for us.
[10:35] Artificial intelligence typically includes a bunch of other tools, in addition to machine learning.
[11:11] We now have more enhanced versions of machine learning that fall under the umbrella of AI, like deep learning, and reinforcement learning, which are all built on top of the idea of machine learning.
[12:12] What are the levels of education that should exist within an organization?
[14:49] What can be automated now that used to not be able to be automated?
[19:03] How GitHub co-pilot can help.
[20:14] What is an AI Factory, and why are people arguing over it?
[21:32] If we can eliminate our busy work, we can essentially get models built quicker, get data science done quicker, and get things automated quicker.
[22:20] The DevOps platform.
[27:40] One of the biggest questions that remain with AI is if we end up with more jobs created as a result of artificial intelligence than are eliminated by it.
[31:32] Okay, let's say how to pronounce data correctly.
Mentioned in this Episode:
Architect Tips — New video podcast!
Clear Measure (Sponsor)
.NET DevOps for Azure: A Developer's Guide to DevOps Architecture the Right Way, by Jeffrey Palermo — Available on Amazon!
Jeffrey Palermo's Twitter — Follow to stay informed about future events!
programming@palermo.network
"Matthew Renze on Data Science for Developers"
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