Today, we are happy to announce that we — Henrik Brink and Joseph Richards, co-founders of wise.io — are writing a Manning book entitled Real-World Machine Learning.
We are writing this book to teach developers and engineers how to successfully build and deploy ML systems in practice. The book provides the essential technical details to get you up and running with ML without inundating you with an overdose of academic theory and complicated math. As we are pushing the boundaries of simplicity and usability in real-world machine learning with wise.io, it is important for us to share our basic processes with the technology community.
We are also excited to announce that Real-World Machine Learning is being featured as Manning’s Deal of the Day today, January 27. This means that you can purchase the book for half off using the code dotd012714au when you purchase from the Manning Website. The offer is valid today until Tuesday morning (Eastern Time).
Filling a gap in ML literature
There are lots of technical and theoretical books about machine learning on the market, but we saw an urgent need for a more practical and accessible treatment of machine learning for developers and engineers. These individuals can benefit tremendously by integrating ML into their projects, but they often lack the know-how to properly set up ML systems on their data. As the title suggests, our book tries to fill this void by focusing on real-world execution of ML.
The book makes as its central theme the end-to-end machine-learning workflow, from data in to insight and predictions out. We also point out practical issues that arise when building and depolying ML systems on real-world data in modern, scalable computing environments. For instance, we tackle problems such as how to properly amass training data, which ML algorithms to apply in different situations, how to appropriately evaluate model performance, and how to make ML systems scale.
Influence the book while we write it
With the Manning Early Access Program (MEAP), you can access chapters as we write them. Follow along as we write about the entire ML workflow, from data to model bulding and evaluation to optimization, feature engineering and scaling up to production-level data flows. Currently, the first two chapters of the book are available on the MEAP site, but stay tuned as new chapters are released over the upcoming weeks.
A forum is set up where you can provide feedback, and we are standing by to answer any questions you might have. We will be posting useful samples and other book updates as we go, so be sure to follow @brinkar, @joeyrichar and @wiseio on Twitter. Of course, we love to hear any feedback from our readers as we roll out the final edition!
Download our case study to learn more about machine learning in action.