Doing good data science

IDENTITY CRISIS
What do machine learning practitioners actually do?
“Any solution to the shortage of machine learning expertise requires answering this question: whether it’s so we know what skills to teach, what tools to build, or what processes to automate.” - fast.ai

MIX IT ALL TOGETHER
Feature-wise transformations
Many real-world problems require integrating multiple sources of information. Feature-wise transformations offer a way to effectively capture and leverage the relationship of various sources, across a wide range of problem settings like image recognition, reinforcement learning, and style transfer. - Distill

UNION IS STRENGTH
Goodbye Microservices: From 100s of problem children to 1 superstar
How and why Segment transitioned their data infrastructure from a microservice architecture to a single, monolithic service. - Segment

STOP THE LINE
Doing good data science
“Moving fast and breaking things is unacceptable if we don’t think about the things we are likely to break. And we need the space to do that thinking: space in project schedules, and space to tell management that a product needs to be rethought.” - O’Reilly

UPHILL BATTLE
The Big Four Reasons Companies Struggle to Hire Data Talent
We hear from both sides of the data talent market from the thousands of data scientists, analysts and others who use Mode every day. Here are four common problems we’ve noticed companies face when hiring for data talent, and how you might fix them. - Mode

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