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 Solutions Architect's Handbook
  • Table Of Contents Toc
  • Feedback & Rating feedback
Solutions Architect's Handbook

Solutions Architect's Handbook - Second Edition

By : Saurabh Shrivastava, Neelanjali Srivastav
4.4 (94)
close
close
Solutions Architect's Handbook

Solutions Architect's Handbook

4.4 (94)
By: Saurabh Shrivastava, Neelanjali Srivastav

Overview of this book

Becoming a solutions architect requires a hands-on approach, and this edition of the Solutions Architect's Handbook brings exactly that. This handbook will teach you how to create robust, scalable, and fault-tolerant solutions and next-generation architecture designs in a cloud environment. It will also help you build effective product strategies for your business and implement them from start to finish. This new edition features additional chapters on disruptive technologies, such as Internet of Things (IoT), quantum computing, data engineering, and machine learning. It also includes updated discussions on cloud-native architecture, blockchain data storage, and mainframe modernization with public cloud. The Solutions Architect's Handbook provides an understanding of solution architecture and how it fits into an agile enterprise environment. It will take you through the journey of solution architecture design by providing detailed knowledge of design pillars, advanced design patterns, anti-patterns, and the cloud-native aspects of modern software design. By the end of this handbook, you'll have learned the techniques needed to create efficient architecture designs that meet your business requirements.
Table of Contents (22 chapters)
close
close
20
Other Books You May Enjoy
21
Index

Machine learning operations

An ML workflow is a set of operations developed and executed to produce a mathematical model, which eventually is designed to solve a real-world problem. But there is no value of these models until they are deployed in production, other than proofs of concept. ML models almost always require deployment to a production environment to provide business value.

At the core, Machine Learning Operations (MLOps) takes an experimental ML model into a production system. MLOps is an emerging practice different from traditional DevOps because the ML development lifecycle and ML artifacts are different. The ML lifecycle involves using patterns from training data, making the MLOps workflow sensitive to data changes, volumes, and quality. Additionally, matured MLOps should support monitoring both ML lifecycle activities and production model monitoring.

MLOps framework implementation makes it simple for organizations to feel confident in building a mature MLOps...

Visually different images
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.
Solutions Architect's Handbook
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