How to grow software development skills in a data science team
Alternatively titled, "how to avoid notebooks-in-production syndrome"
Hello fellow datanistas!
How do we elevate our data science teams from merely experimenting with notebooks to deploying robust, production-quality software? This challenge is one that many of us face in the industry, and I've just penned a blog post that dives deep into this very topic.
In my latest piece, I share insights and strategies from my 7 years of experience in the field, focusing on how we can grow our data science team's software development skillsets. The post covers the importance of tooling that simplifies the transition from notebooks to production and practices that normalize high-quality software development within our teams. It's a comprehensive guide designed to help your team deliver real value to business stakeholders, moving beyond the exploratory phase to actual delivery of value.
But how exactly can we achieve this? And what are the steps to ensure that our work not only meets but exceeds production standards? These are the questions I tackle, providing practical examples and introducing an open-source tool that I've developed, aimed at making these processes smoother and more intuitive. Please find the blog post here!
If you find the insights valuable, please share the post with others in your network who might benefit from it!
Cheers,
Eric