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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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The Machine Learning Solutions Architect Handbook - Create machine learning platforms to run solutions in an enterprise setting (Paperback)
Loot Price: R2,139
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The Machine Learning Solutions Architect Handbook - Create machine learning platforms to run solutions in an enterprise setting (Paperback)
Expected to ship within 10 - 15 working days
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Build highly secure and scalable machine learning platforms to
support the fast-paced adoption of machine learning solutions Key
Features Explore different ML tools and frameworks to solve
large-scale machine learning challenges in the cloud Build an
efficient data science environment for data exploration, model
building, and model training Learn how to implement bias detection,
privacy, and explainability in ML model development Book
DescriptionWhen equipped with a highly scalable machine learning
(ML) platform, organizations can quickly scale the delivery of ML
products for faster business value realization. There is a huge
demand for skilled ML solutions architects in different industries,
and this handbook will help you master the design patterns,
architectural considerations, and the latest technology insights
you'll need to become one. You'll start by understanding ML
fundamentals and how ML can be applied to solve real-world business
problems. Once you've explored a few leading problem-solving ML
algorithms, this book will help you tackle data management and get
the most out of ML libraries such as TensorFlow and PyTorch. Using
open source technology such as Kubernetes/Kubeflow to build a data
science environment and ML pipelines will be covered next, before
moving on to building an enterprise ML architecture using Amazon
Web Services (AWS). You'll also learn about security and governance
considerations, advanced ML engineering techniques, and how to
apply bias detection, explainability, and privacy in ML model
development. And finally, you'll get acquainted with AWS AI
services and their applications in real-world use cases. By the end
of this book, you'll be able to design and build an ML platform to
support common use cases and architecture patterns like a true
professional. What you will learn Apply ML methodologies to solve
business problems Design a practical enterprise ML platform
architecture Implement MLOps for ML workflow automation Build an
end-to-end data management architecture using AWS Train large-scale
ML models and optimize model inference latency Create a business
application using an AI service and a custom ML model Use AWS
services to detect data and model bias and explain models Who this
book is forThis book is for data scientists, data engineers, cloud
architects, and machine learning enthusiasts who want to become
machine learning solutions architects. You'll need basic knowledge
of the Python programming language, AWS, linear algebra,
probability, and networking concepts before you get started with
this handbook.
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