Learn MLOps Basics with This Free eBook

MLOps, short for Machine Learning Operations, has emerged as a crucial discipline for organizations looking to effectively build, deploy, and manage machine learning models. By applying DevOps principles to machine learning workflows, MLOps aims to facilitate seamless collaboration between data scientists and engineers and optimize the end-to-end machine learning lifecycle.

MLOps applies principles and best practices from DevOps to the specific needs of data science and machine learning, facilitating seamless collaboration between data scientists, engineers, and operations teams.

This free ebook, Essential MLOps: What You Need to Know for Successful Implementation, from Data Science Horizons provides a comprehensive introduction to MLOps. You will learn the basics of helping organizations streamline the process of building, deploying, and managing machine learning models in production environments.

Essential MLOps covers the importance of the topic, its key components, essential MLOps skills, tools and technologies, and real-world case studies. It aims to provide readers with a solid understanding of MLOps concepts, techniques, and tools to implement MLOps in their organizations. As the ebook states:

By adopting MLOps practices and leveraging the right tools and technologies, organizations can streamline their machine learning workflows, optimize model performance, and drive meaningful results.

Key topics covered include data management and version control, model training and evaluation, continuous integration and continuous deployment (CI/CD), monitoring and performance management, and case studies from industries like ecommerce, finance, healthcare, and manufacturing. The ebook highlights the benefits of MLOps such as improved collaboration, faster deployment, better model performance, and increased scalability and maintainability of machine learning systems.

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Overall, Essential MLOps: What You Need to Know for Successful Implementation from Data Science Horizons provides valuable insights for anyone looking to successfully implement MLOps within their organization. The practical guidance makes it a useful resource for data scientists, engineers, team leads, and other professionals interested in streamlining machine learning workflows.

 
 
Matthew Mayo (@mattmayo13) is a Data Scientist and the Editor-in-Chief of KDnuggets, the seminal online Data Science and Machine Learning resource. His interests lie in natural language processing, algorithm design and optimization, unsupervised learning, neural networks, and automated approaches to machine learning. Matthew holds a Master's degree in computer science and a graduate diploma in data mining. He can be reached at editor1 at kdnuggets[dot]com.