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AWS for Solutions Architects

AWS for Solutions Architects - Second Edition

By : Saurabh Shrivastava, Neelanjali Srivastav, Alberto Artasanchez, Imtiaz Sayed
4.3 (62)
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AWS for Solutions Architects

AWS for Solutions Architects

4.3 (62)
By: Saurabh Shrivastava, Neelanjali Srivastav, Alberto Artasanchez, Imtiaz Sayed

Overview of this book

The second edition of AWS for Solutions Architects provides a practical guide to designing cloud solutions that align with industry best practices. This updated edition covers the AWS Well-Architected Framework, core design principles, and cloud-native patterns to help you build secure, high-performance, and cost-effective architectures. Gain a deep understanding of AWS networking, hybrid cloud connectivity, and edge deployments. Explore big data processing with EMR, Glue, Kinesis, and MSK, enabling you to extract valuable insights from data efficiently. New chapters introduce CloudOps, machine learning, IoT, and blockchain, equipping you with the knowledge to develop modern cloud solutions. Learn how to optimize AWS storage, implement containerization strategies, and design scalable data lakes. Whether working on simple configurations or complex enterprise architectures, this guide provides the expertise needed to solve real-world cloud challenges and build reliable, high-performing AWS solutions.
Table of Contents (19 chapters)
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Index

Building ML best practices with MLOps

MLOps are the practices and tools used to manage the full lifecycle of ML models, from development to deployment and maintenance. The goal of MLOps is to make deploying ML models to production as seamless and efficient as possible.

Managing an ML application in production requires a robust MLOps pipeline to ensure that the model is continuously updated and relevant as new data becomes available. MLOps helps automate the building, testing, and deploying of ML models. It manages the data and resources used to train and evaluate models, apply mechanisms to monitor and maintain deployed models to detect and address drift, data quality issues, and bias, and finally enables communication and collaboration between data scientists, engineers, and other stakeholders.

The first step in implementing MLOps in AWS is clearly defining the ML workflow, including the data ingestion, pre-processing, model training, and deployment stages. The following...

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