Making artificial intelligence practical, productive, and accessible to everyone. Practical AI is a show in which technology professionals, business people, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics (Machine Learning, Deep Learning, Neural Networks, GANs, MLOps, AIOps, and more). The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest advances in AI, while keeping one foot in the real world, then this is the show for you!
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Large Language Models (LLMs) continue to amaze us with their capabilities. However, the utilization of LLMs in production AI applications requires the integration of private data. Join us as we have a captivating conversation with Jerry Liu from Llam... more
At the recent ODSC East conference, Daniel got a chance to sit down with Erin Mikail Staples to discuss the process of gathering human feedback and creating an instruction tuned Large Language Models (LLM). They also chatted about the importance of o... more
There are a ton of problems around building LLM apps in production and the last mile of that problem. Travis Fischer, builder of open AI projects like @ChatGPTBot, joins us to talk through these problems (and how to overcome them). He helps us unders... more
Honestly, I’m no Boomer, but the “like” thing really has to stop, and I say that with peach and love. It’s off-putting to the more professional listeners and just not a good look in general. You can still be engaging and entertaining. I’d point to your guest Travis Fischer as a really good example to follow. An example NOT to follow was Erin Mikail Staples. While her info was valuable, she was absolutely unlistenable. It was really a chore to get through that show. Cringe barely begins to... more
Processing data is a pretty complex process. This podcast did a pretty good job explaining it. But if you want to learn something more about labeling and annotation, look here https://marketbusinesstimes.com/data-labeling/. In this article, you can find some decent information about raw data processing in machine learning which can really help you down the road.
The guests are often interesting and so is the content, but the delivery makes it unbearable to listen to, like hosts using filler words every other second and getting lost in the arguments. The pod would work for starters in DS/ML and casual listeners but there isn’t enough quality for more senior folks
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this podcast launched 5 years ago and published 226 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.
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