Data Council Blog

Here's our monthly roundup of relevant links for data professionals, from blog posts and tutorials to podcast episodes:

1. Product Management for AI

Peter Skomoroch and Mike Loukides co-authored a very interesting post on what makes product management different in the context of AI. Based on the specificities of AI software development, they make a series of recommendations for a process that also takes business priorities into account. Their post also ends with a list of relevant resources, so it is worth checking out.

Streamlit officially launched out of beta on October 1st, 2019 with the promise to "turn Python scripts into beautiful ML tools." On the same day, Google's AI-focused venture fund Gradient Ventures announced its investment into the startup, which has since then attracted a considerable amount of attention despite its young age.

With COVID-19 forcing almost one billion people to shelter in place around the world, many people have turned to new activities, such as drawing, baking, gardening… or online learning. If that doesn't sound like you, don't feel guilty by any means – sometimes, surviving is enough! But if you want to get more knowledgeable about data science, data engineering and artificial intelligence, we are here for you.

This is why we came up with this list of courses that can help you prepare for a future job in the data field, upgrade your existing skills, or just satisfy your personal curiosity. From free entry-level courses to full-time bootcamps, here's our selection for you to check out:

Here are 10 recent relevant links for data professionals, from blog posts and tutorials to podcast episodes:

1. PyTorch Lightning: a gentle introduction

Former Data Council speaker Will Falcon published an interesting post on PyTorch Lightning, the lightweight PyTorch wrapper born out of his Ph.D. AI research at NYU CILVR and Facebook AI Research (FAIR). Framed as "a gentle introduction", it includes a side-by-side comparison of building a simple MNIST classifier PyTorch and PyTorch Lightning, in order to illustrate how to refactor one into the other. This is highly recommended reading if you are working on AI/ML research, be it as a professional researcher, student or in production.

Your potential salary as a data engineer heavily depends on where you are based; but cost of living also varies around the world. Wondering where you can actually earn more? Let's take a closer look at the United States, Europe and Asia to compare and benchmark data engineering salaries.