Book Image

Solutions Architect’s Handbook - Second Edition

By : Saurabh Shrivastava, Neelanjali Srivastav
4 (2)
Book Image

Solutions Architect’s Handbook - Second Edition

4 (2)
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)
20
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21
Index

Machine learning reference architecture

The following architecture depicts a bank loan approval workflow based on customer data built on the AWS cloud platform.

Here, customer data ingested into the cloud and ML framework decides on the customer loan application.

Figure 14.3: ML architecture in the AWS cloud

In designing the above architecture, some fundamental design principles to consider as a guide are:

  • Training workflow:
    1. Datasets enter the process flow using S3. This data may be raw input data or preprocessed from on-premises datasets.
    2. Ground Truth is used to build a high-quality training labeled dataset for ML models. If required, the data can use the Ground Truth service to label the data.
    3. AWS Lambda can be used for data integration, preparation, and cleaning before datasets are passed to SageMaker.
    4. Data scientists will interface with SageMaker to train and test their models. The Docker images used by SageMaker...