Here's a dark secret about myself as a data scientist. I'm really good with a Jupyter Notebook. It's a great interactive development environment, particularly for data heavy work that needs a lot of eyes on the data, and helps me deal with everyday problems why a column in my data is dirty, or how to get my tick marks to line up properly on a graph. You can do a lot with Jupyter Notebooks. My own Project Firemind was done entirely on Notebooks running on a ridiculous AWS deep learning instance.

But you get to the real world, and it turns out that it doesn't run on notebooks. Business wants you to do something every day. Business wants it to scale. Business wants lots of uptime. And Jupyter Notebooks don't do that. Data science in production is a decent introduction to going from a trainee data scientist working in notebooks, to a real data science working with models hosted on scaling clusters with web end-points. Weber is a data scientist with Zynga, so he knows his stuff. This book is mostly focused on applications, with specific tips on using AWS and Google Cloud Platform. Cloud tech is changing pretty quickly, so I'm sure the specific implementations will change, but this is a solid book of examples if you want to take the next step as professional.

And one note, I bought this book on Kindle and fought with the layout the whole time. The book is well-laid out, but in a way designed for vertical page views rather than flowing text, which makes sense for a programming book. You should get it on pdf.