• 55 minutes 37 seconds
    Why Authenticity Beats Algorithms: The New Rules of Digital Marketing - ML 185
    In this episode, we dive deep into the evolving landscape of digital marketing and brand storytelling. We explore how the intersection of authenticity, community, and technology is reshaping how brands connect with people—and why it's no longer just about the product, but about the experience.

    We talk about how we've shifted our focus from performance-only metrics to a more holistic approach, blending creativity with strategy. There's a big emphasis on human-first marketing—building trust, showing up consistently, and leading with values that resonate.

    We also reflect on the role of content creators and influencers in today’s market, and how brands can partner more meaningfully instead of just transacting for reach. It’s about collaboration, not commodification.

    Key takeaways:
    • Authenticity wins. Audiences can tell when it’s forced.
    • Content isn't king—connection is.
    • Brand loyalty is built through trust, not just a strong call to action.
    • It’s time to ditch the funnel mindset and embrace more circular, relationship-driven marketing.
    • Data is powerful, but gut instinct and creativity still matter—a lot.
    Whether you’re a marketer, entrepreneur, or creator, there’s something in here for you. Let’s keep pushing the industry forward—together.

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    4 April 2025, 1:57 am
  • 51 minutes 26 seconds
    Integrating Business Needs and Technical Skills in Effective Model Serving Deployments - ML 184
    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.

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    13 February 2025, 10:53 pm
  • 55 minutes 8 seconds
    Navigating Common Pitfalls in Data Science: Lessons from Pierpaolo Hipolito - ML 183
    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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    24 January 2025, 3:31 am
  • 42 minutes 6 seconds
    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


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    9 January 2025, 11:57 am
  • 45 minutes 41 seconds
    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.

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    2 January 2025, 11:00 am
  • 31 minutes 58 seconds
    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).

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    26 December 2024, 11:00 am
  • 54 minutes 55 seconds
    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.

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    19 December 2024, 11:00 am
  • 1 hour 12 minutes
    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.


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    12 December 2024, 11:00 am
  • 40 minutes 36 seconds
    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.

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    9 December 2024, 1:31 pm
  • 55 minutes 43 seconds
    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!


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    28 November 2024, 11:00 am
  • 51 minutes 46 seconds
    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.


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    21 November 2024, 11:00 am
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