Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Machine Learning in Microservices
  • Table Of Contents Toc
Machine Learning in Microservices

Machine Learning in Microservices

By : Mohamed Osam Abouahmed, Omar Ahmed
4.7 (10)
close
close
Machine Learning in Microservices

Machine Learning in Microservices

4.7 (10)
By: Mohamed Osam Abouahmed, Omar Ahmed

Overview of this book

With the rising need for agile development and very short time-to-market system deployments, incorporating machine learning algorithms into decoupled fine-grained microservices systems provides the perfect technology mix for modern systems. Machine Learning in Microservices is your essential guide to staying ahead of the curve in this ever-evolving world of technology. The book starts by introducing you to the concept of machine learning microservices architecture (MSA) and comparing MSA with service-based and event-driven architectures, along with how to transition into MSA. Next, you’ll learn about the different approaches to building MSA and find out how to overcome common practical challenges faced in MSA design. As you advance, you’ll get to grips with machine learning (ML) concepts and see how they can help better design and run MSA systems. Finally, the book will take you through practical examples and open source applications that will help you build and run highly efficient, agile microservices systems. By the end of this microservices book, you’ll have a clear idea of different models of microservices architecture and machine learning and be able to combine both technologies to deliver a flexible and highly scalable enterprise system.
Table of Contents (18 chapters)
close
close
1
Part 1: Overview of Microservices Design and Architecture
5
Part 2: Overview of Machine Learning Algorithms and Applications
10
Part 3: Practical Guide to Deploying Machine Learning in MSA Systems

Summary

In this chapter, we went over the general concepts of dataset shifts and how they can negatively impact our machine learning model.

From there, we delved in deeper into what causes these dataset shifts to occur and what different characteristics dataset shifts can exhibit. Using these characteristics, we can better identify the type of dataset shift – whether it was a covariate shift, prior probability shift, or concept shift.

Once we were able to analyze our data and identify the type of dataset shift, we looked at different methods to help us handle and stabilize these dataset shifts so that we could maintain our machine learning model. We went over some techniques, such as feature searching, adversarial search, and density ratio estimation, that can assist us when dealing with dataset shifts.

Using these processes and methods, we can prevent our model from suffering from common dataset shifts that occur in the real world and continuously maintain our machine...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Machine Learning in Microservices
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon