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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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 delve into the journeys and insights of distinguished leaders in the development world. In today's episode, Michael engages with Brian Vallelunga, the visionary CEO of Doppler. Brian shares his compelling journey from early tech innovations to leading multiple startups and eventually founding Doppler, a centralized cloud secret management tool.

In today's episode, Michael and Ben discuss peer review, specifically Michael's experiences. Michael explains his unconventional path, starting with advanced math as a child, then struggling with a math-heavy computer science program in college. He pivoted to environmental studies, focusing on side projects and extracurriculars. These projects led to his first job, and later to a role at a boxing streaming service (2B) with a rigorous peer review process. Ben asks about the importance of the peer review process, and Michael highlights its value in catching errors and ensuring code quality, especially when working under pressure.

In today's episode, Ben and Michael discuss how to handle situations involving individuals lacking expertise in machine learning projects. They explore scenarios where a team lacks expertise, considering approaches for consultants or team members. They discuss various personality types encountered in such situations, including those overly suspicious or resistant to change. Moreover, they discuss how to convince a boss that a proposed project is a bad idea, suggesting a structured approach with clear estimates, risk assessment, and alternative solutions. They emphasize the importance of honesty, transparency, and presenting options with clear pros and cons.

In this episode, Michael and Ben dive deep into the intersection of education and technology with their insightful guest, Daniel Hiterer. Michael, a data engineering and machine learning expert, and Ben, an integrator of Gen AI tools, navigate through Danny's unique perspective on the impact of nurturing educational environments. Currently working at Cornell’s Studio entrepreneurship program, Danny brings a multidisciplinary background, combining history and instructional technology, and shares his vision for the future of learning.