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Practical MLOps: Operationalizing Machine Learning Models

Name: Practical MLOps: Operationalizing Machine Learning Models
Price: 57.99 USD
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Description

Book Synopsis: Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models.

Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start.

You'll discover how to:

Apply DevOps best practices to machine learning
Build production machine learning systems and maintain them
Monitor, instrument, load-test, and operationalize machine learning systems
Choose the correct MLOps tools for a given machine learning task
Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware

Details

Are you tired of spending countless hours trying to get your machine learning models into production? Look no further! Our Practical MLOps: Operationalizing Machine Learning Models Book is the ultimate solution to this common challenge. With a set of proven principles and a reliable and automated approach, MLOps will transform the way you operationalize your machine learning models. Say goodbye to manual processes and hello to efficiency!

What sets MLOps apart from DevOps? Our insightful guide breaks down the key differences and guides you through the implementation process. Whether you're a current or aspiring machine learning engineer, or simply familiar with data science and Python, this book will equip you with the essential tools and methods needed for success. From AutoML to monitoring and logging, we cover it all.

But that's not all. We go beyond theory and show you how to put MLOps into action in leading cloud platforms like AWS, Microsoft Azure, and Google Cloud. By delivering a machine learning system that works faster, you'll have more time and energy to focus on the business problems that truly matter.

One of the many advantages of our book is the practicality it offers. You'll learn how to apply DevOps best practices specifically to machine learning, build production systems that are sustainable, and effectively maintain them. Monitoring, instrumenting, load-testing, and operationalizing your machine learning systems will become second nature.

Curious about the right MLOps tools for different machine learning tasks? We've got you covered. Our guide will steer you in the right direction, ensuring you choose the correct tools for your specific needs. From running machine learning models on various platforms to tapping into specialized hardware, including mobile phones, we leave no stone unturned.

Don't miss out on this incredible opportunity to master MLOps and take your machine learning projects to new heights. Get started on your journey today by purchasing our Practical MLOps: Operationalizing Machine Learning Models Book. Your future success awaits!

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