The MLOps Community with Demetrios Brinkmann - ML048
Demetrios Brinkmann joins the adventure to discuss how he build and supports the MLOps Slack community and online meetups. He goes into the community, moderation, running meetups, sponsorships, and much more.
How to Build and Organize Production ML Solutions ft. Conor Murphy - ML 047
Conor Murphy joins the adventure to explain how he approaches new problems from customers at databricks and how he helps customers see their way past issues with their current solutions to get the outcomes they want.
Using AI to Make the LifeCycle of Software Development Easier ft. Antonio Alegria - ML 046
Antonio Alegria is the head of AI at Outsystems. He leads the effort to find ways to use AI to augment people's experience building software. He joins in to talk about how Outsystems approaches exploring and implementing
AI to make the lifecycle of software development easier.
Multi-modal AI and Machine Teaching ft. Slater Victoroff - ML 045
Slater Victoroff joins the Adventure to discuss mutli-modal AI and machine teaching with the panel. He starts out explaining what multi-modal AI is and how it works. The conversation goes deep before veering into Machine Teaching.
AI Assisted Development ft. Sydney Lai - ML 044
Sydney Lai joins the Adventure to discuss how she and her colleagues build AI assisted features for developers and how that they handle scenarios that they can't always plan for.
Data + ML: What Could Go Wrong? ft. Sandeep Uttamchandani - ML 043
Sandeep Uttamchandani joins the Adventure to discuss the relationships between Data Science and Machine Learning.
He walks through the ways you should set up, manage, and consider the data you use to build and train your Machine Learning systems to get the outcomes that you want.
From Software Engineer to Data Engineer ft. Alexey Grigorev - ML 042
Alexey Grigorev joins the Adventure to discuss how software engineers can begin making the transition from Software Engineer to Data Scientist in their career.
Writing Production Code for ML ft. Ken Youens-Clark - ML 041
Ken Youens-Clark joins the adventure to discuss how to write well factored code with tests to help ML be more approachable. He, Ben, and Chuck discuss what it takes to write good code that runs efficiently, is easy to maintain, and still get ML work done.
Machine Learning for Tabular Data in Practice ft. Mark Ryan - ML 040
Mark Ryan is our first returning guest to the Adventure. He has created a video series for Manning showing how to use Machine Learning for Tabular data.
Transformers and Attention in Machine Learning ft. Ekrem Aksoy - ML 039
Ekrem Aksoy joins the adventure to discuss transformers and the method of helping Machine Learning algorithms focus on the important parts of an image to determine what to do.
Mentorship in Machine Learning - ML 038
Ben Wilson explains the recent developments at DataBricks with their Machine Learning mentorship program for some of their experts. He talks about his approach to helping the Data Scientists and Developers he's mentoring to understand Machine Learning more deeply and gives advice on how others could and should drive their career forward with Machine Learning.
Productionized Machine Learning Engineers featuring Laszlo Sragner - ML 037
Laszlo Sragner joins the adventure to discuss how to make your machine learning approach production ready. The discussion ranges through code quality and how to build and manage your models to keep them production ready and delivering the outcomes you're looking for.
Data Pipelines with Metaflow featuring Ville Tuulos - ML 036
Ville Tuulos is a former Netflix data scientist and engineer who now helps people manage their data pipelines. He's the author of Effective Data Science Infrastructure from Manning publishing and the creator of the Metaflow system for managing data pipelines.
He explains how to think about data and how to plan out how to gather, manage, and transform your data using a system like Metaflow.
Machine Learning: More than Just an Algorithm - ML 035
The conversation starts out asking what’s coming down the pipeline for the Machine Learning community. Ben explains why it’s hard to predict and leads the conversation into what the challenges really are in Machine Learning and the movements across the field to make it more clear on how to get value from your ML setup.
How to Get Started in Machine Learning - ML 034
In this episode, Ben, Francois and Chuck talk about the skills and knowledge that will help you get started with machine learning. Ben outlines 3 different things that will get you started faster than anything else. Francois and Chuck add a couple more things and they discuss the best ways to implement each one of these skills or tactics to become a top notch Machine Learning Engineer.