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

Adventures in Machine Learning

#Tech #Careers #Business #Machine Learning #Artificial Intelligence #Data Analysis #Data Science #Podcasting Education #Ben Wilson, Michael Berk #Ben Wilsons #Michael Berk

22:42

We are here recording live at Medallia Experience at the Wynn in Las Vegas, and have been seeing and hearing some amazing things about how AI can enhance the customer experience as well as enable teams at organizations to create more meaningful connections with customers. Today we’re going to talk about how AI can help to create better experiences for customers before, during, and after their interactions. To help me discuss this topic, I’d like to welcome Fabrice Martin, Chief Product Officer at Medallia. RESOURCES Medallia: https://www.medallia.com Catch the future of e-commerce at eTail Boston, August 11-14, 2025. Register now: https://bit.ly/etailboston and use code PARTNER20 for 20% off for retailers and brands Don't Miss MAICON 2025, October 14-16 in Cleveland - the event bringing together the brights minds and leading voices in AI. Use Code AGILE150 for $150 off registration. Go here to register: https://bit.ly/agile150 Connect with Greg on LinkedIn: https://www.linkedin.com/in/gregkihlstrom Don't miss a thing: get the latest episodes, sign up for our newsletter and more: https://www.theagilebrand.show Check out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology: https://www.agilebrandguide.com The Agile Brand podcast is brought to you by TEKsystems. Learn more here: https://www.teksystems.com/versionnextnow The Agile Brand is produced by Missing Link—a Latina-owned strategy-driven, creatively fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. https://www.missinglink.company…

大约1年之前 31:58

MP3•单集首页

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

209集单集

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

Adventures in Machine Learning

54 subscribers

published 大约1年之前

MP3•单集首页

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

209集单集

#Tech #Careers #Business #Machine Learning #Artificial Intelligence #Data Analysis #Data Science #Podcasting Education #Ben Wilson, Michael Berk #Ben Wilsons #Michael Berk

所有剧集

1
Why Authenticity Beats Algorithms: The New Rules of Digital Marketing - ML 185 55:37

17 minutes之前55:37

55:37

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

1
Integrating Business Needs and Technical Skills in Effective Model Serving Deployments - ML 184 51:26

7 weeks之前51:26

51:26

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 .…

1
Navigating Common Pitfalls in Data Science: Lessons from Pierpaolo Hipolito - ML 183 55:08

10 weeks之前55:08

55:08

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. 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 42:06

12 weeks之前42:06

42:06

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 .…

1
A/B Testing with ML ft. Michael Berk - ML 181 45:41

13 weeks之前45:41

45:41

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 Models LinkedIn: Michael Berk Michael Berk - Medium Picks Ben- David Thorne Books Charles- Shadow Hunter Michael- Stuart Russell Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support .…

1
Navigating Build vs. Buy Decisions in Emerging AI Technologies - ML 180 31:58

14 weeks之前31:58

31:58

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 Wilson LinkedIn: Michael Berk Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support .…

1
Artificial Intelligence as a Service with Peter Elger and Eóin Shanaghy - ML 179 54:55

15 weeks之前54:55

54:55

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 fourTheorem Twitter: Eóin Shanaghy Twitter: Peter Elger Picks Charles- The Eye of the World: Book One of The Wheel of Time by Robert Jordan Charles - Changemakers With Jamie Atkinson Charles- Podcast Domination Show by Luis Diaz Charles- Buzzcast Charles- Podcast Talent Coach Eóin- IKEA | IDÅSEN Desk sit/stand, black/dark gray63x31 1/2 " Eóin- Kinesis | Freestyle2 Split- Adjustable Keyboard for PC Peter- The Wolfram Physics Project Peter- PBS Space Time Peter- Youtube Channel | 3Blue1Brown Peter- Cracking the Code Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support .…

1
Combating Burnout in Machine Learning: Strategies for Balance and Collaboration - ML 178 1:12:04

16 weeks之前1:12:04

1:12:04

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 Wilson LinkedIn: Michael Berk Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support .…

1
The Nature of the World and AI with Rishal Hurbans - ML 177 40:36

17 weeks之前40:36

40:36

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. Links Kaggle: Your Machine Learning and Data Science Community Rishal Hurbans Inktober Book giveaway link Picks Chuck- Hero with a thousand faces by Joseph Campbell Chuck- Masterbuilt smoker Rishal-Learn something new everyday Rishal- Building a StoryBrand by Donald Miller Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support .…

1
Crafting Data Solutions: Shrinking Pie and Leveraging Insights for Optimal Data Learning - ML 176 55:43

18 weeks之前55:43

55:43

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 .…

1
Challenges and Solutions in Managing Code Security for ML Developers - ML 175 51:46

19 weeks之前51:46

51:46

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 Berk LinkedIn: Ben Wilson Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support .…

1
Innovative Security Solutions for Developers - ML 174 58:26

20 weeks之前58:26

58:26

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. Brian emphasizes the importance of making security tools enticing for developers, comparing it to making vegetables taste like candy, to boost productivity. His team’s strategy revolves around seamless integration into developers’ workflows, featuring a VS Code extension and automatic syncing akin to Dropbox, enhancing efficiency and ease of use. They explore Doppler's competitive edge and how it partners with major cloud resource managers, making two-click integrations effortless. Brian also discusses their customer-centric development approach and the release of enterprise features like two-person approval and config inheritance, designed for complex organizational needs. Brian's entrepreneurial journey is marked by significant pivots driven by frustration and market demand, rather than strategic planning alone. He shares candid thoughts on the impact of founders and success, cautioning against the allure of celebrity status and emphasizing team contributions. Join them as they dive into insightful discussions on building developer-friendly security tools, the nuances of secret management, and Brian's perspectives on startup growth and innovation. Discover how Doppler is revolutionizing secret management, one integration at a time. Socials LinkedIn: Brian Vallelunga Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support .…

1
Peer Review and Career Development - ML 173 1:08:09

21 weeks之前1:08:09

1:08:09

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. Moreover, Ben discusses the learning experience at different career stages, noting that junior developers learn from senior developers' code and feedback. Ben discusses the differences in peer review for different types of code changes. They discuss the importance of thorough review for critical code changes and many more! Socials LinkedIn: Ben Wilson LinkedIn: Michael Berk Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support .…

1
Navigating Expertise Gaps - ML 172 1:16:15

22 weeks之前1:16:15

1:16:15

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. The discussion then returns to the Gen AI time-series case study, suggesting a presentation of multiple options, including established algorithms and the Gen AI approach, to facilitate a data-driven decision. Finally, the episode addresses the scenario of a teammate being untrained about a system they built, suggesting a combination of direct but constructive feedback and a collaborative approach to identify the root cause of the issue. Socials LinkedIn Ben Wilson LinkedIn Michael Berk Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support .…

1
The Influence of Gen AI on Personalized Education and Curiosity - ML 171 1:08:19

23 weeks之前1:08:19