Machine Learning Engineering with Python - Published on 5th Nov!

Not long to go now until Machine Learning Engineering with Python is published on 5th November, which happens to be Bonfire Night in the UK. I look forward to seeing lots of fireworks and pretending its to celebrate my book being finally out!

Ahead of publication I wanted to thank so many of you for your kind words of encouragement and for, of course, supporting the book. I really hope it does help people working in the machine learning space in some small way .

Ahead of publication, I wanted to share a little teaser from Chapter 1: Introduction to ML Engineering, where I talk about what I believe is important to consider when doing machine learning 'in the real world'. I hope you enjoy the snippet and that you enjoy the book!

"The majority of us who work in machine learning, analytics, and related disciplines do so for for-profit companies. It is important therefore that we consider some of the important aspects of doing this type of work in the real world.

First of all, the ultimate goal of your work is to generate value. This can be calculated and defined in a variety of ways, but fundamentally your work has to improve something for the company or their customers in a way that justifies the investment put in. This is why most companies will not be happy for you to take a year to play with new tools and then generate nothing concrete to show for it (not that you would do this anyway, it is probably quite boring) or to spend your days reading the latest papers and only reading the latest papers. Yes, these things are part of any job in technology, and especially any job in the world of machine learning, but you have to be strategic about how you spend your time and always be aware of your value proposition.

Secondly, to be a successful ML engineer in the real world, you cannot just understand the technology; you must understand the business. You will have to understand how the company works day to day, you will have to understand how the different pieces of the company fit together, and you will have to understand the people of the company and their roles. Most importantly, you have to understand the customer, both of the business and of your work. If you do not know the motivations, pains, and needs of the people you
are building for, then how can you be expected to build the right thing?

Finally, and this may be controversial, the most important skill for you being a successful ML engineer in the real world is one that this book will not teach you, and that is the ability to communicate effectively. You will have to work in a team, with a manager, with the wider community and business, and, of course, with your customers, as mentioned above. If you can do this and you know the technology and techniques (many of which are
discussed in this book), then what can stop you?"