Deploy Machine Learning Models To Production
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Deploy Machine Learning Models To Production
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Author : Pramod Singh
language : en
Publisher:
Release Date : 2021
Deploy Machine Learning Models To Production written by Pramod Singh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Computer programming categories.
Build and deploy machine learning and deep learning models in production with end-to-end examples. This book begins with a focus on the machine learning model deployment process and its related challenges. Next, it covers the process of building and deploying machine learning models using different web frameworks such as Flask and Streamlit. A chapter on Docker follows and covers how to package and containerize machine learning models. The book also illustrates how to build and train machine learning and deep learning models at scale using Kubernetes. The book is a good starting point for people who want to move to the next level of machine learning by taking pre-built models and deploying them into production. It also offers guidance to those who want to move beyond Jupyter notebooks to training models at scale on cloud environments. All the code presented in the book is available in the form of Python scripts for you to try the examples and extend them in interesting ways. You will: Build, train, and deploy machine learning models at scale using Kubernetes Containerize any kind of machine learning model and run it on any platform using Docker Deploy machine learning and deep learning models using Flask and Streamlit frameworks.
A Design Pattern For Deploying Machine Learning Models To Production
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Author : Runyu Xu
language : en
Publisher:
Release Date : 2020
A Design Pattern For Deploying Machine Learning Models To Production written by Runyu Xu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Machine Learning (ML) becomes increasingly popular; industry spends billions of dollars building ML systems. Data scientists have come up with many good algorithms and trained models. However, putting those ML models into production is still in the early stage. The deployment process is distinct from that for traditional software applications; it is not yet well understood among data scientists and IT engineers in their roles and responsibilities, resulting in many anti-pattern practices [21]. The key issues identified by researchers at Google[40] include lack of production-like prototyping stack for data scientists, monolithic programs not fitted for component based ML system orchestration, and lack of best practices in system design. To find solutions, teams need to understand the inherent structure of ML systems and to find ML engineering best practices. This paper presents an abstraction of ML system design process, a design pattern named Model-Service-Client + Retraining (MSC/R) consisting of four main components: Model (data and trained model), Service (model serving infrastructure), Client (user interface), and Retraining (model monitoring and retraining). Data scientists and engineers can use this pattern as a discipline in designing and deploying ML pipelines methodically. They can separate concerns, modularize ML systems, and work in parallel. This paper also gives case studies on how to use MSC/R to quickly and reliably deploy two ML models -- YOLOv3, an object detection model, and Stock Prediction using Long Short-Term Memory (LSTM) algorithm onto AWS and GCP clouds. Two different implementation approaches are used: serving the model as a microservice RESTful API on AWS managed container platform ECS, and on GCP serverless platform Cloud Run. In the end, this paper gives analysis and discussion on how using the MSC/R design pattern helps to meet the objectives of implementing ML production systems and solve the common problems. It also provides insights and recommendations.
Machine Learning Model Serving Patterns And Best Practices
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Author : Md Johirul Islam
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-12-30
Machine Learning Model Serving Patterns And Best Practices written by Md Johirul Islam and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-30 with Computers categories.
Become a successful machine learning professional by effortlessly deploying machine learning models to production and implementing cloud-based machine learning models for widespread organizational use Key FeaturesLearn best practices about bringing your models to productionExplore the tools available for serving ML models and the differences between themUnderstand state-of-the-art monitoring approaches for model serving implementationsBook Description Serving patterns enable data science and ML teams to bring their models to production. Most ML models are not deployed for consumers, so ML engineers need to know the critical steps for how to serve an ML model. This book will cover the whole process, from the basic concepts like stateful and stateless serving to the advantages and challenges of each. Batch, real-time, and continuous model serving techniques will also be covered in detail. Later chapters will give detailed examples of keyed prediction techniques and ensemble patterns. Valuable associated technologies like TensorFlow severing, BentoML, and RayServe will also be discussed, making sure that you have a good understanding of the most important methods and techniques in model serving. Later, you'll cover topics such as monitoring and performance optimization, as well as strategies for managing model drift and handling updates and versioning. The book will provide practical guidance and best practices for ensuring that your model serving pipeline is robust, scalable, and reliable. Additionally, this book will explore the use of cloud-based platforms and services for model serving using AWS SageMaker with the help of detailed examples. By the end of this book, you'll be able to save and serve your model using state-of-the-art techniques. What you will learnExplore specific patterns in model serving that are crucial for every data science professionalUnderstand how to serve machine learning models using different techniquesDiscover the various approaches to stateless servingImplement advanced techniques for batch and streaming model servingGet to grips with the fundamental concepts in continued model evaluationServe machine learning models using a fully managed AWS Sagemaker cloud solutionWho this book is for This book is for machine learning engineers and data scientists who want to bring their models into production. Those who are familiar with machine learning and have experience of using machine learning techniques but are looking for options and strategies to bring their models to production will find great value in this book. Working knowledge of Python programming is a must to get started.
Mlops Fundamentals
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Author : Booker Blunt
language : en
Publisher: Independently Published
Release Date : 2025-07-11
Mlops Fundamentals written by Booker Blunt and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-11 with Computers categories.
Unlock the power of machine learning in production environments. MLOps Fundamentals is your comprehensive guide to deploying, managing, and scaling machine learning models in production. Whether you're an aspiring MLOps engineer, data scientist, or software developer, this book equips you with the foundational knowledge and practical tools to move machine learning models from experimentation to real-world deployment. You'll learn how to build end-to-end machine learning pipelines, automate workflows, and monitor model performance in production. From model training and testing to versioning and deployment, MLOps Fundamentals covers it all-ensuring your models run smoothly, efficiently, and securely. Inside, you'll discover how to: Set up a complete MLOps workflow using tools like Docker, Kubernetes, and CI/CD Automate model training, testing, and deployment with MLFlow and Kubeflow Version and manage models using tools like DVC and ModelDB Build robust pipelines that handle data preprocessing, training, and deployment Monitor and manage deployed models for performance, accuracy, and drift Scale machine learning models with cloud platforms like AWS, Google Cloud, and Azure Implement model rollback, A/B testing, and continuous integration strategies Ensure security and governance in an MLOps environment Collaborate with teams effectively using best practices in MLOps culture With hands-on examples, code snippets, and real-world scenarios, this book helps you apply MLOps principles to make machine learning models production-ready and scalable. Whether you're deploying models for web apps, customer insights, or predictive maintenance, MLOps Fundamentals provides the knowledge and tools to bring your AI models to life in production.
Ultimate Mlops For Machine Learning Models
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Author : Saurabh Dorle
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2024-08-30
Ultimate Mlops For Machine Learning Models written by Saurabh Dorle and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-30 with Computers categories.
TAGLINE The only MLOps guide you'll ever need KEY FEATURES ● Acquire a comprehensive understanding of the entire MLOps lifecycle, from model development to monitoring and governance. ● Gain expertise in building efficient MLOps pipelines with the help of practical guidance with real-world examples and case studies. ● Develop advanced skills to implement scalable solutions by understanding the latest trends/tools and best practices. 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. WHAT WILL YOU LEARN ● Implement and manage end-to-end machine learning lifecycles. ● Utilize essential tools and technologies for MLOps effectively. ● Design and optimize data pipelines for efficient model training. ● Develop and train machine learning models with best practices. ● Deploy, monitor, and maintain models in production environments. ● Address scalability challenges and solutions in MLOps. ● Implement robust security practices to protect your ML systems. ● Ensure data governance, model compliance, and security in ML operations. ● Understand emerging trends in MLOps and stay ahead of the curve. WHO IS THIS BOOK FOR? This book is for data scientists, machine learning engineers, and data engineers aiming to master MLOps for effective model management in production. It’s also ideal for researchers and stakeholders seeking insights into how MLOps drives business strategy and scalability, as well as anyone with a basic grasp of Python and machine learning looking to enter the field of data science in production. 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
Deploying Machine Learning Models With Fastapi And Onnx
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Author : Maurice H Connor
language : en
Publisher: Independently Published
Release Date : 2025-12-16
Deploying Machine Learning Models With Fastapi And Onnx written by Maurice H Connor and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-16 with Computers categories.
Deploying Machine Learning Models with FastAPI and ONNX: A Practical Guide to Scalable AI Applications Are you ready to bring your machine learning models to life? If the idea of deploying AI feels daunting, you're not alone. Many beginners find the deployment phase of machine learning to be one of the most intimidating challenges. But don't worry this book will guide you, step by step, through the process in a way that's both approachable and empowering. Whether you're a developer eager to level up your skills or a beginner with no prior technical experience, Deploying Machine Learning Models with FastAPI and ONNX is the perfect companion for your journey into scalable AI applications. This practical, hands-on guide is designed to take you from the basics to production-ready deployment, even if you're starting from scratch. What's Inside This Book? No Technical Jargon, Just Practical Steps: You don't need a background in AI or complex coding languages to get started. Every concept is explained in simple, easy-to-follow steps that build your confidence and skills as you go. Real-World Applications: You'll learn how to deploy machine learning models into production with FastAPI and ONNX. By the end of this book, you'll be equipped to serve real-time predictions in a scalable, reliable way-skills you can apply immediately to real-world projects. Step-by-Step Guidance: This book is structured to take you through each stage of the deployment pipeline-from preparing and training your model to integrating it into a fast, efficient API. No more overwhelming theory-only practical, actionable advice. Celebrate Small Wins: Mistakes are a part of the learning process, and in this book, we embrace them! You'll see how to troubleshoot common challenges and celebrate your progress as you deploy your first models. Comprehensive, Yet Accessible: Designed for both beginners and developers looking to expand their knowledge, this guide breaks down every step and provides you with the tools and support needed to succeed. Key Benefits You'll Gain: Master the fundamentals of deploying AI models using FastAPI and ONNX. Build production-ready APIs for real-time model serving and scalable AI applications. Learn how to handle real-world challenges like model performance, optimization, and inference speed. Get comfortable with model versioning, error handling, and continuous integration. Gain practical experience with deployment on cloud platforms and edge devices. Learn to debug, test, and scale your AI applications with confidence. Why This Book is Different: Beginner-Friendly: No need to be an expert in machine learning or coding to follow along. The friendly tone and approachable style make complex concepts easier to grasp. Hands-On Learning: Focused on practical, real-world applications, this book will teach you skills that are immediately useful and in-demand in the tech industry. Scalable Solutions: You'll learn to deploy models not just for testing, but in real production environments where they can scale to meet user needs. Start Your Journey Today Whether you're exploring AI for the first time or seeking a structured way to level up your deployment skills, this book is your ultimate guide to fast, efficient, and scalable machine learning deployments. Ready to transform your knowledge into real-world applications? Grab your copy today, and let's get your machine learning models deployed and serving real-time predictions in no time!
Kubernetes For Machine Learning
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Author : Mark J Jaynes
language : en
Publisher: Independently Published
Release Date : 2025-11-22
Kubernetes For Machine Learning written by Mark J Jaynes and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-22 with Computers categories.
Unlock the full power of AI in production with Kubernetes the backbone of modern, scalable machine learning systems. Machine learning doesn't end when a model is trained. The real challenge begins when you deploy, scale, and monitor it in the real world. Kubernetes for Machine Learning: Deploying, Scaling, and Monitoring AI Models in Production is your practical guide to building reliable, high-performance ML systems using the industry's most trusted orchestration platform. This book breaks down the complexities of Kubernetes into clear, actionable steps tailored specifically for AI engineers, data scientists, DevOps teams, and MLOps practitioners. From containerizing ML workloads to automating pipelines, optimizing GPU usage, handling model rollouts, and observing real-time performance, you'll learn how to create production environments that can handle modern AI demands with ease. Whether you're running deep learning models, large language models, or high-throughput inference services, this guide equips you with the tools and patterns needed to ship models confidently and keep them running smoothly at scale. Benefits: End-to-end MLOps workflow: Learn how to package, deploy, and manage ML models in Kubernetes clusters. Scalable infrastructure: Master autoscaling, GPU scheduling, load balancing, and resource optimization for AI workloads. Production-grade monitoring: Implement logging, tracing, model performance tracking, and drift detection with modern observability tools. Real-world patterns: Follow proven architectures for serving APIs, batch jobs, streaming pipelines, and multi-model systems. Cloud and hybrid-ready: Apply concepts across AWS, GCP, Azure, on-prem Kubernetes, or hybrid setups. Ready to take your machine learning projects from experimentation to reliable production? Get your copy now and start building scalable, automated, and monitored AI systems with Kubernetes.
Learn Tensorflow 2 0
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Author : Pramod Singh
language : en
Publisher: Apress
Release Date : 2019-12-17
Learn Tensorflow 2 0 written by Pramod Singh and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-17 with Computers categories.
Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters. You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting ways. What You'll Learn Review the new features of TensorFlow 2.0 Use TensorFlow 2.0 to build machine learning and deep learning models Perform sequence predictions using TensorFlow 2.0 Deploy TensorFlow 2.0 models with practical examples Who This Book Is For Data scientists, machine and deep learning engineers.
Machine Learning And Deep Learning Using Python And Tensorflow
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Author : Venkata Reddy Konasani
language : en
Publisher: McGraw Hill Professional
Release Date : 2021-04-29
Machine Learning And Deep Learning Using Python And Tensorflow written by Venkata Reddy Konasani and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-29 with Technology & Engineering categories.
Understand the principles and practices of machine learning and deep learning This hands-on guide lays out machine learning and deep learning techniques and technologies in a style that is approachable, using just the basic math required. Written by a pair of experts in the field, Machine Learning and Deep Learning Using Python and TensorFlow contains case studies in several industries, including banking, insurance, e-commerce, retail, and healthcare. The book shows how to utilize machine learning and deep learning functions in today’s smart devices and apps. You will get download links for datasets, code, and sample projects referred to in the text. Coverage includes: Machine learning and deep learning concepts Python programming and statistics fundamentals Regression and logistic regression Decision trees Model selection and cross-validation Cluster analysis Random forests and boosting Artificial neural networks TensorFlow and Keras Deep learning hyperparameters Convolutional neural networks Recurrent neural networks and long short-term memory
Production Ready Applied Deep Learning
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Author : Tomasz Palczewski
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-08-30
Production Ready Applied Deep Learning written by Tomasz Palczewski and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-30 with Computers categories.
Supercharge your skills for developing powerful deep learning models and distributing them at scale efficiently using cloud services Key Features Understand how to execute a deep learning project effectively using various tools available Learn how to develop PyTorch and TensorFlow models at scale using Amazon Web Services Explore effective solutions to various difficulties that arise from model deployment Book Description Machine learning engineers, deep learning specialists, and data engineers encounter various problems when moving deep learning models to a production environment. The main objective of this book is to close the gap between theory and applications by providing a thorough explanation of how to transform various models for deployment and efficiently distribute them with a full understanding of the alternatives. First, you will learn how to construct complex deep learning models in PyTorch and TensorFlow. Next, you will acquire the knowledge you need to transform your models from one framework to the other and learn how to tailor them for specific requirements that deployment environments introduce. The book also provides concrete implementations and associated methodologies that will help you apply the knowledge you gain right away. You will get hands-on experience with commonly used deep learning frameworks and popular cloud services designed for data analytics at scale. Additionally, you will get to grips with the authors' collective knowledge of deploying hundreds of AI-based services at a large scale. By the end of this book, you will have understood how to convert a model developed for proof of concept into a production-ready application optimized for a particular production setting. What you will learn Understand how to develop a deep learning model using PyTorch and TensorFlow Convert a proof-of-concept model into a production-ready application Discover how to set up a deep learning pipeline in an efficient way using AWS Explore different ways to compress a model for various deployment requirements Develop Android and iOS applications that run deep learning on mobile devices Monitor a system with a deep learning model in production Choose the right system architecture for developing and deploying a model Who this book is for Machine learning engineers, deep learning specialists, and data scientists will find this book helpful in closing the gap between the theory and application with detailed examples. Beginner-level knowledge in machine learning or software engineering will help you grasp the concepts covered in this book easily.