241 episodes

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!

Practical AI: Machine Learning, Data Science Changelog Media

    • Technology
    • 4.3 • 121 Ratings

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!

    Automate all the UIs!

    Automate all the UIs!

    Dominik Klotz from askui joins Daniel and Chris to discuss the automation of UI, and how AI empowers them to automate any use case on any operating system. Along the way, the trio explore various approaches and the integration of generative AI, large language models, and computer vision.

    • 43 min
    Fine-tuning vs RAG

    Fine-tuning vs RAG

    In this episode we welcome back our good friend Demetrios from the MLOps Community to discuss fine-tuning vs. retrieval augmented generation. Along the way, we also chat about OpenAI Enterprise, results from the MLOps Community LLM survey, and the orchestration and evaluation of generative AI workloads.

    • 58 min
    Automating code optimization with LLMs

    Automating code optimization with LLMs

    You might have heard a lot about code generation tools using AI, but could LLMs and generative AI make our existing code better? In this episode, we sit down with Mike from TurinTech to hear about practical code optimizations using AI “translation” of slow to fast code. We learn about their process for accomplishing this task along with impressive results when automated code optimization is run on existing open source projects.

    • 45 min
    The new AI app stack

    The new AI app stack

    Recently a16z released a diagram showing the “Emerging Architectures for LLM Applications.” In this episode, we expand on things covered in that diagram to a more general mental model for the new AI app stack. We cover a variety of things from model “middleware” for caching and control to app orchestration.

    • 45 min
    Blueprint for an AI Bill of Rights

    Blueprint for an AI Bill of Rights

    In this Fully Connected episode, Daniel and Chris kick it off by noting that Stability AI released their SDXL 1.0 LLM! They discuss its virtues, and then dive into a discussion regarding how the United States, European Union, and other entities are approaching governance of AI through new laws and legal frameworks. In particular, they review the White House’s approach, noting the potential for unexpected consequences.

    • 41 min
    Vector databases (beyond the hype)

    Vector databases (beyond the hype)

    There’s so much talk (and hype) these days about vector databases. We thought it would be timely and practical to have someone on the show that has been hands on with the various options and actually tried to build applications leveraging vector search. Prashanth Rao is a real practitioner that has spent and huge amount of time exploring the expanding set of vector database offerings. After introducing vector database and giving us a mental model of how they fit in with other datastores, Prashanth digs into the trade offs as related to indices, hosting options, embedding vs. query optimization, and more.

    • 51 min

Customer Reviews

4.3 out of 5
121 Ratings

121 Ratings

Sad_Truth ,

Pretty good, but Daniel needs to stop saying “like”

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 describe it.

Mikoo231 ,

Your Essential Tool for AI Mastery

If you're searching for an AI podcast that does more than just report the news, "Practical AI" is an absolute must-listen! As an AI consultant myself, I've found immense value in its insightful content that goes far beyond what typical AI podcasts offer. With a unique blend of hands-on advice and expert perspectives, this podcast equips you with knowledge that you can directly apply in your professional journey. For those immersed in the world of AI, "Practical AI" serves not just as a source of information, but as a tool for growth and advancement. Give it a listen, and let it transform your understanding of AI!

GOPGambler ,

Not great for the layman

I wanted to like this podcast since it seemed to be geared toward the layman. But unfortunately, it way too jargony and, like so many experts, the host understands stands the concepts well, but does not know how to communicate them clearly to people who do not share his background in the material. One example is the mini-series on p values where as best I can tell, he never actually defines the p value at all. While I appreciate the hosts efforts to try and bring this information to the public, it is not working for me.

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