Adventures in Machine Learning

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.
On YouTubeMoving from Dev Notebooks to Production Code - ML 098
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

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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

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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

Links

• GitHub: Corey-Zumar

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Become 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. 

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Do you multitask? If so, you'll want to check out this episode. We'll cover...

1. The difference between good and bad context switching
2. Some personal stories about context switching on projects
3. Practical tips you can leverage to improve your productivity

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

Advertising Inquiries: https://redcircle.com/brands

Privacy & Opt-Out: https://redcircle.com/privacy

Become 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...

1. The difference between applied ML and ML frameworks
2. How to effectively scope projects via Heilmeier's Catechism

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

Advertising Inquiries: https://redcircle.com/brands

Privacy & Opt-Out: https://redcircle.com/privacy

Become 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.

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

Advertising Inquiries: https://redcircle.com/brands

Privacy & Opt-Out: https://redcircle.com/privacy

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

Do you multitask? If so, you'll want to check out this episode. We'll cover...

Sponsors

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. 

Sponsors

• Top End Devs
• Coaching | Top End Devs

Links

• Dean Pleban

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Become 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...

1. What are tests and why should you use them?
2. What's the difference between unit tests and integration tests?
3. What should you test?
4. How should you write tests in python? (the answer is to use pytest)

Sponsors

• Top End Devs
• Coaching | Top End Devs
• Enov8, who provides test data management

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Privacy & Opt-Out: https://redcircle.com/privacy

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