Adventures in Machine Learning

Auteur(s): Charles M Wood
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  • Résumé

  • Machine Learning is growing in leaps and bounds both in capability and adoption. Listen to our experts discuss the ideas and fundamentals needed to succeed as a Machine Learning Engineer.

    Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.
    Copyright Charles M Wood
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Gestion et leadership Économie
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Épisodes
  • Integrating Business Needs and Technical Skills in Effective Model Serving Deployments - ML 184

    Integrating Business Needs and Technical Skills in Effective Model Serving Deployments - ML 184
    Feb 13 2025
    Welcome back to another episode of Adventures in Machine Learning, where hosts Michael Berk and Ben Wilson delve into the intricate process of implementing model serving solutions. In this episode, they explore a detailed case study focused on enhancing search functionality with a particular emphasis on a hot dog recipe search engine. The discussion takes you through the entire development loop, beginning with understanding product requirements and success criteria, moving through prototyping and tool selection, and culminating in team collaboration and stakeholder engagement. Michael and Ben share their insights on optimizing for quick signal in design, leveraging existing tools, and ensuring service stability. If you're eager to learn about effective development strategies in machine learning projects, this episode is packed with valuable lessons and behind-the-scenes engineering perspectives. Join us as we navigate the challenges and triumphs of building impactful search solutions.

    Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.
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    51 min
  • Navigating Common Pitfalls in Data Science: Lessons from Pierpaolo Hipolito - ML 183

    Navigating Common Pitfalls in Data Science: Lessons from Pierpaolo Hipolito - ML 183
    Jan 24 2025
    Welcome to another insightful episode of Top End Devs, where we delve into the fascinating world of machine learning and data science. In this episode, host Charles Max Wood is joined by special guest Pierpaolo Hipolito, a data scientist at the SAS Institute in the UK. Together, they explore the intriguing paradoxes of data science, discussing how these paradoxes can impact the accuracy of machine learning models and providing insights on how to mitigate them.

    Pierpaolo shares his expertise on causal reasoning in machine learning, drawing from his master's research and contributions to Towards Data Science and other notable publications. He elaborates on the complexities of data modeling during the early stages of the COVID-19 pandemic, highlighting the use of simulation and synthetic data to address data sparsity.

    Throughout the conversation, the focus remains on the importance of understanding the underlying system being modeled, the role of feature engineering, and strategies for avoiding common pitfalls in data science. Whether you are a seasoned data scientist or just starting out, this episode offers valuable perspectives on enhancing the reliability and interpretability of your machine learning models.

    Tune in for a deep dive into the paradoxes of data science, practical advice on feature interaction, and the importance of accurate data representation in achieving meaningful insights.


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    55 min
  • Cows, Camels, and the Human Brain - ML 182

    Cows, Camels, and the Human Brain - ML 182
    Jan 9 2025
    What do cows and camels have to do with the human brain? The latest developments in machine learning, of course! In this episode, Michael and Ben dive into a new white paper from Facebook AI researchers that reveals a LOT about the future of modeling. They discuss “cows and camels”, the question of predictive vs causal modeling, and how algorithms are getting scary good at emulating the human brain these days.
    In This Episode
    Why Facebook’s new research is VERY exciting for AI learning and causality (but what does it have to do with cows and camels?)
    The answer to “Is predictive or causal modeling more accurate?” (and why it’s not the best question to ask)
    Not sure if you need machine learning or just plain data modeling? Michael lays it out for you
    What algorithms are learning about human behavior to accurately emulate the human brain in 2022 and beyond


    Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.
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    42 min

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