Best Environment for Machine Learning
Episode 25: Show Notes
Lately, there’s been a lot of hype about AI. In today’s podcast, we too are going to chat about AI, and specifically the subset of artificial intelligence called machine learning. Instead of talking about the political, social, and moral aspects of this subject, however, we’re going to speak about some of the more mundane aspects of deploying this technology. Tuning in, you’ll hear about some of the recent technologies that have been the subject of hype cycles, what Logan learned about the hype cycle of crypto and how it affected AWS, and how this relates to the current hype cycles of AIML. We then discuss the ML and production experience of the three major cloud platforms (AWS, GCP, and Azure), the ML APIs that these companies have made available, and how you can extend these APIs for your unique requirements. To learn more about how to differentiate between the different cloud providers, the importance of being able to update existing models, the necessity for the automated collection and evaluation of the current model, and so much more, tune in today!
Key Points From This Episode:
Tweetables:
“Maybe some of these companies like OpenAI will emerge as major players moving forward, but I think we can be sure that one of the big winners is guaranteed to be the cloud platforms.” — Logan Gallagher [0:04:01]
“That is the real use case that we can identify for ML; the ability to extend the capabilities of the working software we have.” — Jon Gallagher [0:06:14]
“What’s maybe more important than deploying a model for production is having the ability to update that model.” — Logan Gallagher [0:22:39]
“With ML and AI, there is a temptation to treat this as something new and different, but I really see all of the important software practices that we’ve been learning and implementing over the years, still applying here.” — Logan Gallagher [0:29:01]
Links Mentioned in Today’s Episode:
Best Environment for Machine Learning
Episode 25: Show Notes
Lately, there’s been a lot of hype about AI. In today’s podcast, we too are going to chat about AI, and specifically the subset of artificial intelligence called machine learning. Instead of talking about the political, social, and moral aspects of this subject, however, we’re going to speak about some of the more mundane aspects of deploying this technology. Tuning in, you’ll hear about some of the recent technologies that have been the subject of hype cycles, what Logan learned about the hype cycle of crypto and how it affected AWS, and how this relates to the current hype cycles of AIML. We then discuss the ML and production experience of the three major cloud platforms (AWS, GCP, and Azure), the ML APIs that these companies have made available, and how you can extend these APIs for your unique requirements. To learn more about how to differentiate between the different cloud providers, the importance of being able to update existing models, the necessity for the automated collection and evaluation of the current model, and so much more, tune in today!
Key Points From This Episode:
Tweetables:
“Maybe some of these companies like OpenAI will emerge as major players moving forward, but I think we can be sure that one of the big winners is guaranteed to be the cloud platforms.” — Logan Gallagher [0:04:01]
“That is the real use case that we can identify for ML; the ability to extend the capabilities of the working software we have.” — Jon Gallagher [0:06:14]
“What’s maybe more important than deploying a model for production is having the ability to update that model.” — Logan Gallagher [0:22:39]
“With ML and AI, there is a temptation to treat this as something new and different, but I really see all of the important software practices that we’ve been learning and implementing over the years, still applying here.” — Logan Gallagher [0:29:01]
Links Mentioned in Today’s Episode: