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.
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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
Should you Context Switch when Writing Code? - ML 095
Do you multitask? If so, you'll want to check out this episode. We'll cover... The difference between good and bad context switching Some personal stories about context switching on projects 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
How To Recession Proof Your Job - BONUS
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.
Important Questions To Ask When Scoping ML Projects - ML 094
Have you ever wondered how to prioritize your ML projects? Today we will talk about... The difference between applied ML and ML frameworks 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
How To Do Research Spikes - ML 093
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
Should you Context Switch when Writing Code? - ML 093
Do you multitask? If so, you'll want to check out this episode. We'll cover... The difference between good and bad context switching Some personal stories about context switching on projects 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
How to Simplify Data Science with DagsHub Founders - ML 092
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 LinksDean Pleban
How to Test ML Code - ML 091
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) Sponsors Top End Devs Coaching | Top End Devs
AGI, Neuron Simulators, and More with Charles Simon - ML 090
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 background Classical algorithms vs manual design Knowledge and power generated in our brains AI and ML concepts and learning patterns Graph based approaches and deep learning “Terminator” and AGI Future AI and open source simulator Leveraging technology to solve problems Sponsors Top End Devs Coaching | Top End Devs Links The Team LinkedIn: Charles Simon
Complex ML Models with Data Scientist Fernando Lopez - ML 089
Fernando Lopez joins the show today to share his ML insights with a video interview recruiting platform for candidate hiring. Michael and Ben also deep dive into various related ML models and AI topics. In this episode… Core software engineering skills Practice data algorithms and structures Working towards production-grade ML Data engineering and data structures Using state-of-the-art models Applying labels to data The AI revolution Eliminating bias and unweighted data set Unconscious and conscious exercises Complex models with structure Sponsors Top End Devs Coaching | Top End Devs Links How to Package and Distribute Machine Learning Models with MLFlow - KDnuggets Twitter: @ferneutronn
Distributed Time Series in Machine Learning - ML 088
Today the panel discusses high level distributed time series models, using a hot dog stand company as the case study to anchor the understanding with these models. In this episode… Understanding use case ML flow models and events KPI forecasts Metadata outputs Prediction intervals for hotdog data Automated time series forecasts Libraries required for optimization Practical tips managing the data and Setting up the data for consumption Managing black swan events Sponsors Top End Devs Coaching | Top End Devs
Time Series Models in Machine Learning - ML 087
Today on the show, the panel discusses time series models, practical tips and tricks, and shares stories and examples of various models and the processes for optimal application in your ML workflows. In this episode… Ben’s time series model for sales forecasting The flat line model Examples using time series models Understanding your data Lag functions and moving averages Signal processing in models Manually adding change points Deep learning in time series models Sponsors Top End Devs Coaching | Top End Devs
Optical Character Recognition (OCR) and Machine Learning with Ahmad Anis - ML 086
Optical character recognition, or OCR for short, is used to describe algorithms and techniques (both electronic and mechanical) to convert images of text to machine-encoded text. Today on the show, Ahmad Anis shares how he applies Machine Learning to OCR for small hardware applications, for example, blurring a face in a video in real time or on a stream to safeguard privacy using AI. The panel also discusses various strategies related to learning and soft skills needed for success within the industry. In this episode… Optical character recognition (OCR) defined Multiprocessing vs. multithreading I/O bound tasks vs. CPU tasks How to handle a retry in Python Strategies for employing on small hardware Template matching and preprocessing Gray scaling integrations How to learn and get started within the industry Reducing the scope and industry soft skills Sponsors Top End Devs Coaching | Top End Devs Links LinkedIn: Ahmad Anis Twitter: @AhmadMustafaAn1