Today we speak with a software engineer who is interested in becoming an ML engineer. Expect to learn about ML roles that are most attainable based on a strong software engineering skill set. We also cover some tangible strategies you can leverage to make the transition.

Today we look at an applied use case for ML: developing intelligent meeting notes. Expect to learn about LLMs, AI assistants, and how to develop an AI startup.

Today we deep dive into the mind of two brilliant Databricks software engineers. Their primary project was building the model serving feature, but expect to learn about ML side projects, traits of successful software engineers, and much more!

Hosts of the Adventures in DevOps podcast, Jillian Rowe and Jonathan Hall, join Ben and Michael on this week's episode crossover. They talk about the intersection of ML and DevOps. They dive into the concepts and differences between ML and DevOps. Additionally, they talk about how ML ideas may be applied to DevOps principles and vice versa.

ChatGPT is the most robust free chatbot. It can answer questions, write code, and summarize text. Today we will talk about the creation of ChatGPT, its implications for society, and how the model actually works.

Today we look at an applied use case for ML: parsing movie scripts. Expect to learn about bringing ML to new industries, the future of Large Language Models (LLM), and automation in the movie industry.

"Any sufficiently advanced technology is indistinguishable from magic." Today, Michael and Ben talk about the broad implications of ChatGPT and similar algorithms. Expect to learn about...

Today we speak with a staff data scientist at Walmart who specializes in forecasting. He has built an open-source tool that allows you to leverage tabular data in PyTorch. He also has written a book on time series forecasting with deep learning.

How do you develop ML code? Do you use notebooks or do you use IDEs? In this episode, we get some practical advice from both Ben and our guest on leveraging software principles to write better code in both an IDE and notebook environment. We'll also learn about a cool new Databricks feature that will help you run ML code from an IDE.

In this week's episode, we meet with Micheal McCourt, the head of engineering at SigOpt. He is an industry expert on optimization algorithms, so expect to learn about constraint-active search, SigOpt's new open-source optimizer, and how to run an engineering team.

Have you ever wondered how to secure a cloud deployment? Well, today we talk to the president at a cloud security company about personal security, detecting malicious actors, startup trends, and much more!

Have you ever wondered about the most promising industries in Machine Learning? Today we will learn from Avi Goldfarb, the chair of AI at the University of Toronto, about... -The most promising AI industries -Potential problems with powerful AI -The economics behind innovation

In this episode, Ben talks with Rosaria Silipo, a Software Engineer and Developer Relations advocate at Knime. They discuss the benefits of low-code ML, delve into the history of ML development work as it has changed over the past few decades, and discuss a few stories about the importance of pursuing simplicity in implementations.

In this week's episode we meet with Mike Arov, committer to the MLOps tool framework lineapy. From the benefits of notebooks as development tooling for Data Science work to the complex refactoring needed to convert them to production-capable code bases, our conversation dives deep into the generally under-represented bridge tooling of code base conversions.

Let's be honest. We've all copy and pasted code from the internet. There are many great code sources and in this episode we discuss how to leverage existing code. We'll explain how to understand a code base and some best practices for contribution.