Adventures in Machine Learning

Charles M Wood
  • 4.7 (13)
  • 科技
  • 两周一更
Adventures in Machine Learning

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.

  1. Cows, Camels, and the Human Brain - ML 182

    6天前

    Cows, Camels, and the Human Brain - ML 182

    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.

    42 分钟
  2. A/B Testing with ML ft. Michael Berk - ML 181

    1月2日

    A/B Testing with ML ft. Michael Berk - ML 181

    Michael Berk joins the adventure to discuss how he uses Machine Learning within the context of A/B testing features within applications and how to know when you have a viable test option for your setup. Links How to Find Weaknesses in your Machine Learning ModelsLinkedIn: Michael BerkMichael Berk - Medium Picks Ben- David Thorne BooksCharles- Shadow HunterMichael- Stuart Russell Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

    46 分钟
  3. Navigating Build vs. Buy Decisions in Emerging AI Technologies - ML 180

    2024/12/26

    Navigating Build vs. Buy Decisions in Emerging AI Technologies - ML 180

    In today's episode, we dive into the critical decision-making process of building versus buying technology solutions, especially when it comes to agentic logic-based frameworks. With the industry still in its early stages, I recommend waiting for managed solutions to mature, while Ben suggests the educational value of simple project builds. They discuss the importance of understanding the technology thoroughly before diving into business-focused decisions, using tools like customer user journeys (CUJs) to evaluate scalability, cost-efficiency, and maintainability. They also highlight some initial challenges and missteps in project management and the necessity for pre-evaluation by tech teams. For non-technical teams engaged in technical projects, they provide structured guidance on navigating these unknowns efficiently. Additionally, they emphasize the value of research spikes and incremental development to manage risk and learn from user behavior. Finally, they explore the promising yet evolving landscape of generative AI and its potential high ROI with Retrieval-Augmented Generation (RAG). Socials Linkedin: Ben WilsonLinkedIn: Michael Berk Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

    32 分钟
  4. Artificial Intelligence as a Service with Peter Elger and Eóin Shanaghy - ML 179

    2024/12/19

    Artificial Intelligence as a Service with Peter Elger and Eóin Shanaghy - ML 179

    Peter Elger and Eóin Shanaghy join Charles Max Wood to dive into what Artificial Intelligence and Machine Learning related services are available for people to use. Peter and Eóin are experts in AWS and explain what is provided in its services, but easily extrapolate to other clouds. If you're trying to implement Artificial Intelligence algorithms, you may want to use or modify an algorithm already built and provided to you. Links fourTheoremTwitter: Eóin ShanaghyTwitter: Peter Elger Picks Charles- The Eye of the World: Book One of The Wheel of Time by Robert JordanCharles - Changemakers With Jamie AtkinsonCharles- Podcast Domination Show by Luis DiazCharles- BuzzcastCharles- Podcast Talent CoachEóin- IKEA | IDÅSEN Desk sit/stand, black/dark gray63x31 1/2 "Eóin- Kinesis | Freestyle2 Split- Adjustable Keyboard for PCPeter- The Wolfram Physics ProjectPeter- PBS Space TimePeter- Youtube Channel | 3Blue1BrownPeter- Cracking the Code Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

    55 分钟
  5. Combating Burnout in Machine Learning: Strategies for Balance and Collaboration - ML 178

    2024/12/12

    Combating Burnout in Machine Learning: Strategies for Balance and Collaboration - ML 178

    In this episode, Ben and Michael explore burnout, particularly in machine learning and data science. They highlight that burnout stems from exhaustion, cynicism, and inefficiency and can be caused by repetitive tasks, overwhelming workloads, or being in the wrong role. They also tackle strategies to combat burnout, including collaborating with others, mentoring, shifting focus between tasks, and hiring more people to distribute the workload. A key takeaway is the importance of knowledge sharing and not hoarding tasks for job security, as this can lead to burnout and inefficiency. They also discuss managing burnout and its components, particularly exhaustion, cynicism, and inefficiency, through personal experiences. Finally, they talk about how burnout can lead to inefficiency and physical manifestations, like a lack of motivation to engage in activities outside of work. Socials  LinkedIn: Ben WilsonLinkedIn: Michael Berk  Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

    1 小时 12 分钟
  6. The Nature of the World and AI with Rishal Hurbans - ML 177

    2024/12/09

    The Nature of the World and AI with Rishal Hurbans - ML 177

    Rishal Hurbans is the author of Grokking Artificial Intelligence Algorithms. He walks us through how to learn different Machine Learning algorithms. He also then walks us through the different types of algorithms based on different natural systems and processes. LinksKaggle: Your Machine Learning and Data Science CommunityRishal HurbansInktoberBook giveaway link PicksChuck- Hero with a thousand faces by Joseph CampbellChuck- Masterbuilt smokerRishal-Learn something new everydayRishal- Building a StoryBrand by Donald Miller Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

    41 分钟
  7. Crafting Data Solutions: Shrinking Pie and Leveraging Insights for Optimal Data Learning - ML 176

    2024/11/28

    Crafting Data Solutions: Shrinking Pie and Leveraging Insights for Optimal Data Learning - ML 176

    In today’s episode, Michael and Ben are joined by industry expert Barzan Mozafari, the CEO and co-founder at Keebo. He delves deep into the evolving landscape of data learning and cloud optimization. They explore how understanding data distribution can lead to early detection of anomalies and how optimizing data workflows can result in significant cost savings and unintended business growth. Barzan sheds light on leveraging existing cloud technologies and the role of automated tools in enhancing system interactions, while Ben talks about the intricacies of platform migration and tech debt. They dig into the challenges and strategies for optimizing complex data pipelines, the economic pressures faced by data teams, and insights into innovation stemming from academic research. The conversation also covers the importance of maintaining customer trust without compromising data security and the iterative nature of both academic and industrial approaches to problem-solving. Join them as they navigate the intersection of technical debt, AI-driven optimization, and the dynamic collaboration between researchers and engineers, all aimed at driving continuous improvement and innovation in the world of data. So, gear up for an episode packed with insights on shrinking pie data learning, cloud costs, automated optimization tools, and much more. Let’s dive right in! Socials LinkedIn: Barzan Mozafari Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

    56 分钟
  8. Challenges and Solutions in Managing Code Security for ML Developers - ML 175

    2024/11/21

    Challenges and Solutions in Managing Code Security for ML Developers - ML 175

    Today, join Michael and Ben as they delve into crucial topics surrounding code security and the safe execution of machine learning models. This episode focuses on preventing accidental key leaks in notebooks, creating secure environments for code execution, and the pros and cons of various isolation methods like VMs, containers, and micro VMs. They explore the challenges of evaluating and executing generated code, highlighting the risks of running arbitrary Python code and the importance of secure evaluation processes. Ben shares his experiences and best practices, emphasizing human evaluation and secure virtual environments to mitigate risks. The episode also includes an in-depth discussion on developing new projects with a focus on proper engineering procedures, and the sophisticated efforts behind Databricks' Genie service and MLflow's RunLLM. Finally, Ben and Michael explore the potential of fine-tuning machine learning models, creating high-quality datasets, and the complexities of managing code execution with AI. Tune in for all this and more as we navigate the secure pathways to responsible and effective machine learning development. Socials LinkedIn: Michael BerkLinkedIn: Ben Wilson Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

    52 分钟
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4.7
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关于

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.

信息

  • 创作者
    Charles M Wood
  • 活跃年份
    2020年 - 2025年
  • 单集
    206
  • 分级
    儿童适宜
  • 版权
    © Copyright Charles M Wood
  • 节目网站
    Adventures in Machine Learning

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