Ever feel like you can’t see the forest through the trees? We get it. In this episode, Michael sits down with Maria Zentsova, an ML developer and data analyst who teaches us how to get a handle on our data.

In this episode, Ben and Michael cover more of Shreya Shankar’s deep dive into ML monitoring, including the biggest production challenges, what you NEED to know about adversarial attacks, and how to conduct effective tests and never make past mistakes again.

If you’re feeling a little nervous about your baby leaving the nest, we get it. In this episode, Ben and Daniel talk with Abhilash Pattnaik, where they discuss the ONE fact about ML you can’t forget, the do’s and don’ts about applying alerts, and the often-forgotten truth about data drift.

Ready to dive DEEP into predictive modeling? You’ve come to the right podcast. In this episode, Ben and Michael sit down with Maarit Widmann, a data scientist whose bread and butter is making models more accurate. They discuss how to effectively use confusion matrices and other tools, why you need to avoid THIS misconception to get accurate churn rates, and the BIG question you should be asking if your data seems off.

Mo’ advancements mean mo’ problems, and today, Michael and Ben are diving into the biggest issues of ML monitoring in 2022. They lay out the ELEVEN (cause nothing good comes easy) potential pitfalls that you should know this year, the important questions that you NEED to ask yourself before launching your baby, and this ONE phenomenon that reveals a fundamental flaw in models versus the real deal.

If you’re looking for a team that actually cares about the issues you’re facing, look no further than Databricks, and they’ve got something exciting out. In this episode, Michael and Ben welcome on the development team of MLflow, an open-source lifecycle manager for machine learning. They cover how Databricks is redefining how developers and engineers collaborate, the reason behind Databricks’ crazy success, and the number ONE most important testing structure for any development team.

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.

Want to know how businesses are measuring their success nowadays? In this episode, Ben talks with Michael Berk about how businesses are leveraging numerous performance metrics to stay on top of their game. The two emphasize what your FIRST step should be, why A/B testing is not just for marketers, and the “elephant in the room” that determines how KPIs should be integrated.

Michael and Ben talk about Prediction Intervals. They discuss how to put intervals around your predictions and then aiming for a certain level of confidence.

Mani has summarized hundreds of business books that outline how to build, grow, and operate a business and he shares his expertise with Chuck and the listeners in this special episode. Chuck and Mani discuss what it takes to be a successful entrepreneur. They talk about their businesses on a regular basis and Chuck's been getting a lot of requests for entrepreneurship help. He and Mani talk about the 3 primary things that add momentum to your business and help you keep the momentum up when setbacks come your way.

Ben and Chuck put their areas of expertise together to discuss how you could build Machine Learning into web and mobile applications. They discuss the various ways data scientists and Machine Learning engineers can use their expertise to build engines and provide data and results to web and mobile developers.

Mani provides us with strategies and tactics to get Deep Work time and how to get our minds into that focused state for hours at a time. He has read hundreds of books that have taught him the secrets to getting more done by getting into this state.

Aliaksei Mikhailiuk joins the Adventure to discuss the tools he wishes he'd mastered before getting his PhD in Machine Learning. However, the conversation meanders through his background, what qualifications people should have, algorithms, and approaches to machine learning along with the tools discussion.

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.

Pier Paolo Ippolito joins the adventure to discuss some of the paradoxes or counterintuitive generalizations people make about their datasets. Ben, Chuck, and Pier dive into how to look at statistical data and how to identify trends in the data.