Machine Learning is growing in leaps and bounds both in capability and adoption. Listen to our experts discuss the ideas and fundamentals needed to succeed as a Machine Learning Engineer.
Machine Learning for Meeting Notes - ML 110
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.On YouTubeMachine Learning for Meeting Notes - ML 110SponsorsChuck's Resume TemplateDeveloper Book Club startingBecome a Top 1% Dev with a Top End Devs MembershipSocialsLinkedIn: Artem Koren Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy
Model Serving at Databricks - ML 109
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!SponsorsChuck's Resume TemplateDeveloper Book Club startingBecome a Top 1% Dev with a Top End Devs MembershipSocialLinkedIn: Ankit MathurLinkedIn: Sue Ann HongAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy
Where ML and DevOps Meet - ML 108
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.SponsorsChuck's Resume TemplateDeveloper Book Club startingBecome a Top 1% Dev with a Top End Devs MembershipAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy
How Does ChatGPT Work? - ML 107
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. SponsorsChuck's Resume TemplateDeveloper Book Club startingBecome a Top 1% Dev with a Top End Devs MembershipAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy
Machine Learning for Movie Scripts - ML 106
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.SponsorsChuck's Resume TemplateDeveloper Book Club startingBecome a Top 1% Dev with a Top End Devs MembershipLinksLinkedIn: Ruslan KhamidullinAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy
ChatGPT and the Divine - ML 105
"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...The difference between AI and MLGeneral Artificial IntelligenceSome personal opinions about the overlap between "the divine" and AISponsorsChuck's Resume TemplateDeveloper Book Club startingBecome a Top 1% Dev with a Top End Devs MembershipAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy
Deep Learning for Tabular and Time Series Data - ML 104
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.SponsorsChuck's Resume TemplateDeveloper Book Club startingBecome a Top 1% Dev with a Top End Devs MembershipLinks[2207.08548] GATE: Gated Additive Tree Ensemble for Tabular Classification and RegressionModern Time Series Forecasting with Python: Explore industry-ready time series forecasting using modern machine learning and deep learningLinkedIn: Manu JosephTwitter: @manujosephvGitHub: manujosephvAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy
Notebooks vs. IDEs With Fabian Jakobs - ML 103
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.SponsorsChuck's Resume TemplateDeveloper Book Club startingBecome a Top 1% Dev with a Top End Devs MembershipLinks Databricks extension for Visual Studio Code | Databricks on AWSLinkedIn: Fabian JakobsLinkedIn: DatabricksAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy
How to think about Optimization - ML 102
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.SponsorsChuck's Resume TemplateDeveloper Book Club startingBecome a Top 1% Dev with a Top End Devs MembershipLinksMichael McCourt | SigOptLinkedIn: Michael McCourtAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy
Protecting Your ML From Phishing And Hackers - ML 101
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!SponsorsChuck's Resume TemplateDeveloper Book Club starting with Clean Architecture by Robert C. MartinBecome a Top 1% Dev with a Top End Devs MembershipLinksLinkedIn: Kevin Dominik KorteTwitter: @KeDKorte Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy
The Disruptive Power of Artificial Intelligence - ML 100
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 industriesPotential problems with powerful AIThe economics behind innovationSponsorsChuck's Resume TemplateDeveloper Book Club starting with Clean Architecture by Robert C. MartinBecome a Top 1% Dev with a Top End Devs MembershipLinksPower and Prediction: The Disruptive Economics of Artificial IntelligenceAvi GoldfarbLinkedIn: Avi Goldfarb Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy
A History Of ML And How Low Code Tooling Accelerates Solution Development - ML 099
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.SponsorsChuck's Resume TemplateDeveloper Book Club starting with Clean Architecture by Robert C. MartinBecome a Top 1% Dev with a Top End Devs MembershipLinks7 Things You Didn’t Know You Could do with a Low Code ToolLinkedIn: Rosaria SilipoTwitter: @DMR_RosariaAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy
Moving from Dev Notebooks to Production Code - ML 098
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. Sponsors Chuck's Resume Template Developer Book Club starting with Clean Architecture by Robert C. Martin Become a Top 1% Dev with a Top End Devs Membership Links Is There a Way to Bridge the MLOps Tools Gap? - KDnuggets Lineapy.org
How to Edit and Contribute to Existing Code Base - ML 097
Let's be honest. We've all copied and pasted code from the internet. There are many great code sources and in this episode, Ben and Michael discuss how to leverage existing code. They explain how to understand a code base and some best practices for contribution. Sponsors Chuck's Resume Template Developer Book Club starting with Clean Architecture by Robert C. Martin Become a Top 1% Dev with a Top End Devs Membership
MLflow 2.0 And How Large-Scale Projects Are Managed In The Open Source - ML 096
Corey Zumar talks about the new release of MLflow, 2.0, and what the new major features that are included in the release. Bilal and Corey then discuss managing feature implementation priorities, and selling large-scale project ideas to internal customers, end-users, executives, and the dev team. The discussion also centers around generalizing feature requests to implementations that will work for the masses and how to effectively do prototype releases for incremental agile development for complex projects. Sponsors Chuck's Resume Template Developer Book Club starting with Clean Architecture by Robert C. Martin Become a Top 1% Dev with a Top End Devs Membership LinksGitHub: Corey-Zumar