Adventures in Machine Learning

Guest Mike Arov discusses challenges transitioning ML projects from notebooks to production code, emphasizing tools like Linear Pi. Topics include bridging ML Ops and DevOps, managing software versions, automating artifact generation, and tracing dependencies in notebooks. The conversation highlights the importance of collaboration in open-source projects and the significance of automation tools in streamlining deployment processes.

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.  SponsorsChuck's Resume TemplateDeveloper Book Club starting with Clean Architecture by Robert C. MartinBecome a Top 1% Dev with a Top End Devs MembershipAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacyBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

Corey Zumar talks about MLflow 2.0's major features. They discuss managing feature priorities, selling large-scale project ideas, generalizing feature requests, and prototype releases for agile development.

Do you multitask? If so, you'll want to check out this episode. We'll cover...The difference between good and bad context switchingSome personal stories about context switching on projectsPractical tips you can leverage to improve your productivitySponsorsChuck's Resume TemplateDeveloper Book Club starting with Clean Architecture by Robert C. MartinBecome a Top 1% Dev with a Top End Devs MembershipAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacyBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

Get the Black Friday/Cyber Monday "Focus Blocks Bundle" Deal Coupon Code: "THRIVE" for a GIANT discount   Are you looking at all the layoffs and uncertainty going on and wondering if your company is the next to cut back?  Or, maybe you're a freelancer or entrepreneur who is trying to figure out how to deliver more value to gain or retain customers?  Mani Vaya joins Charles Max Wood to discuss the one thing that both of them use to more than double their productivity on a daily basis.  Mani has read 1,000's of productivity books over the last several years and has formulated a methodology for getting more done, but found that he lacked the discipline to follow through on his plans.  The he found the one thing that kept him on track and made him so productive that he is now getting all of his work done and was able to live the life he wants.  Chuck also weighs in on how Mani's technique has worked for him and allows him to spend more time with his wife and kids, run a podcast network, and a nearly full time contract.  Join the episode to learn how Chuck and Mani get into a regular flow state with their work and consistently deliver at work.  Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacyBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

Have you ever wondered how to prioritize your ML projects? Today we will talk about...The difference between applied ML and ML frameworksHow to effectively scope projects via Heilmeier's Catechism SponsorsChuck's Resume TemplateDeveloper Book Club starting with Clean Architecture by Robert C. MartinBecome a Top 1% Dev with a Top End Devs MembershipAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacyBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

Have you ever wondered how to efficiently learn topics? In this episode, we discuss how to conduct a research spike within an ML team setting. SponsorsChuck's Resume TemplateDeveloper Book Club starting with Clean Architecture by Robert C. MartinBecome a Top 1% Dev with a Top End Devs MembershipAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacyBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

Have you ever wondered why data science is hard? Well, in this episode we cover some common data science challenges and how the founders of DagsHub are looking to solve them.  SponsorsTop End DevsCoaching | Top End DevsLinksDean PlebanAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacyBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

In this show, we cover some practical tips for writing reliable ML code. Here are some of the questions we look to answer...What are tests and why should you use them?What's the difference between unit tests and integration tests?What should you test?How should you write tests in python? (the answer is to use pytest)SponsorsTop End DevsCoaching | Top End DevsEnov8, who provides test data managementAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacyBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

Charles Simon, BSEE, MSCs is a nationally recognized entrepreneur and software developer who has many years of computer experience in industry including pioneering work in artificial intelligence (AI).  Mr. Simon's technical experience includes the creation of two unique AI systems along with software for successful neurological test equipment combining AI development with biomedical nerve signal testing that gives him the singular insight.  Today on the show, Charles, Michael, and Ben explore the riveting future of AGI and other illuminating technology concepts.  This is an exciting episode you won’t want to miss! In this episode…Charles Simon’s extensive backgroundClassical algorithms vs manual designKnowledge and power generated in our brainsAI and ML concepts and learning patternsGraph based approaches and deep learning“Terminator” and AGIFuture AI and open source simulatorLeveraging technology to solve problems SponsorsTop End DevsCoaching | Top End DevsLinksThe TeamLinkedIn: Charles SimonAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacyBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.