With AI and machine learning creeping in every industry, the paradigm of IT as we know it is also changing, ML is a natural extension of the software revolution we have seen in the last decades, knowing how to utilize ML in your industry will be a key element for success and growth in the coming years.

This transformation will need a new vision, as new jobs, new platforms and new ways of doing business will emerge from it. I believe at this point we are past the hype of AI and we are in the middle of a reality where machine learning and inference are helping thousands of businesses grow and prosper.

I have read several books on AI and ML, and the two that stands out are:

• Human + Machine , reimagining work in the age of AI
• Pragmatic AI : an introduction to Cloud-Based Machine learning.

either you are an engineer, a manager, an executive, or merely driven by curiosity about AI and ML, I recommend that you read these books to fully grasp its impact on many industries

Human + Machine, reimagining work in the age of AI

Paul R. Daugherty and H. James Wilson did an amazing job at reimaging what work will look like in the age of AI, they introduced the notion of the Missing Middle; a realistic approach of looking at this transformation by defining what machine can do, what humans can do, and  where humans and machines have hybrid activities.

Humans can judge, lead, empathize and create, machines can iterate, predict and adapt.

AI can give humans superpowers, but humans need to train, sustain machines, and at times explain its decisions.

Paul and James talk about an entirely new set of jobs that will emerge from this alliance.

Pragmatic AI : an introduction to Cloud-Based Machine learning

if you are an engineer who likes to understand how the training and the inference work under the hood, this book would be a great resource for you.

Pragmatic AI explains how you can utilize cloud resources in AWS Azure, and GCP to train your models, optimize them, and deploy a production scale machine learning powered application.

the book also contains real applications and code samples to help reproduce it on your own, and it covers the following topics :

• AI and ML toolchain: from python ecosystem toolchains like numpy,  Jupiter Notebooks and others to the tools available on AWS GCP and Azure
• DevOps practices to help you deliver and deploy
• Creating practical AI applications from scratch
• Optimization

 

there are definitely a lot of publications concerning AI and ML, but the combination of the two books above will cover the organizational and structural challenges that an organization will face when it comes to adopting AI, and also the technical backgrounds needed to work with it.