Ultimate MLOps for Machine Learning Models
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Ultimate MLOps for Machine Learning Models  (English, Paperback, D. Dorle Saurabh)

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Highlights
  • Language: English
  • Binding: Paperback
  • Publisher: Orange Education Pvt Ltd
  • Genre: Computers
  • ISBN: 9788197651205
  • Edition: 1, 2024
  • Pages: 314
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  • Description
    This book is an essential resource for professionals aiming to streamline and optimize their machine learning operations. This comprehensive guide provides a thorough understanding of the MLOps life cycle, from model development and training to deployment and monitoring. By delving into the intricacies of each phase, the book equips readers with the knowledge and tools needed to create robust, scalable, and efficient machine learning workflows. Key chapters include a deep dive into essential MLOps tools and technologies, effective data pipeline management, and advanced model optimization techniques. The book also addresses critical aspects such as scalability challenges, data and model governance, and security in machine learning operations. Each topic is presented with practical insights and real-world case studies, enabling readers to apply best practices in their job roles. Whether you are a data scientist, ML engineer, or IT professional, this book empowers you to take your machine learning projects from concept to production with confidence. It equips you with the practical skills to ensure your models are reliable, secure, and compliant with regulations. By the end, you will be well-positioned to navigate the ever-evolving landscape of MLOps and unlock the true potential of your machine learning initiatives.
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    Specifications
    Book Details
    Imprint
    • Orange Education Pvt Ltd
    Publication Year
    • 2024 Augest
    Book Type
    • Non Fiction
    Table of Contents
    • 1. Introduction to MLOps 2. Understanding Machine Learning Lifecycle 3. Essential Tools and Technologies in MLOps 4. Data Pipelines and Management in MLOps 5. Model Development and Training 6. Model Optimization Techniques for Performance 7. Efficient Model Deployment and Monitoring Strategies 8. Scalability Challenges and Solutions in MLOps 9. Data, Model Governance, and Compliance in Production Environments 10. Security in Machine Learning Operations 11. Case Studies and Future Trends in MLOps Index
    Contributors
    Author Info
    • Saurabh Dorle holds a Master’s degree in Machine Learning from the Maharashtra Institute of Technology, Pune, and a Bachelor’s degree in Computer Engineering from Pune University, Pune. He is a distinguished expert in the field of Data Science. Over the past six years, Saurabh has amassed extensive experience, leading and developing end-to-end solutions that have delivered substantial value across diverse industries, including media and entertainment, telecom, retail, and E-commerce. Saurabh has made significant contributions to the domains of Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP). His published research work in these areas has earned widespread recognition, showcasing his dedication to advancing the frontiers of technology. His innovative solutions have optimized processes and enhanced decision-making capabilities within organizations, driving tangible business outcomes and fostering growth. He actively shares his expertise with the broader community through insightful blogs. These writings not only disseminate cutting-edge knowledge but also inspire and educate aspiring data scientists. His commitment to continuous learning and innovation is evident in every aspect of his professional journey. Beyond his professional achievements, Saurabh enjoys reading books and playing the guitar in his free time. This balance of professional rigor and personal enrichment underscores his holistic approach to life and work. His unwavering commitment to excellence and vision for the future of ML make him a trusted guide in the evolving landscape of MLOps.
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