Mlflow For Machine Learning Operations
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Mlflow For Machine Learning Operations
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Author : William Smith
language : en
Publisher: HiTeX Press
Release Date : 2025-08-19
Mlflow For Machine Learning Operations written by William Smith and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-19 with Computers categories.
"MLflow for Machine Learning Operations" "MLflow for Machine Learning Operations" is an authoritative guide that illuminates the principles and practicalities of deploying robust machine learning solutions in modern organizations. It opens with a comprehensive survey of the MLOps landscape, addressing the full lifecycle from experiment tracking and reproducibility, to production governance and compliance. Readers are carefully introduced to the challenges inherent in operationalizing machine learning—such as scalability, automation, security, and integration—before delving deep into why and how MLflow emerges as the central platform for orchestrating these workflows. The book offers an in-depth exploration of MLflow’s modular capabilities: from experiment tracking and artifact management, to reproducible packaging using MLflow Projects, model logging and deployment for diverse frameworks, and robust lifecycle management with the Model Registry. Through practical strategies and architectural patterns, it details how MLflow can be seamlessly integrated into enterprise CI/CD pipelines, storage, and compute infrastructure, while also highlighting advanced topics such as automated model validation, access control, audit trails, and observability at production scale. Further strengthening its value, the volume examines key ecosystem integrations and operational best practices for security, compliance, and cost governance. Real-world patterns for federated, multi-cloud, and edge ML operations are illustrated, alongside forward-looking guidance on explainable AI, bias mitigation, and emerging trends in MLOps. Whether for ML engineers, data scientists, or technology leaders, this essential resource empowers readers to harness MLflow for efficient, secure, and scalable machine learning operations across their organizations.
Hands On Machine Learning With C
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Author : Kirill Kolodiazhnyi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-01-24
Hands On Machine Learning With C written by Kirill Kolodiazhnyi 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 2025-01-24 with Computers categories.
Apply supervised and unsupervised machine learning algorithms using C++ libraries, such as PyTorch C++ API, Flashlight, Blaze, mlpack, and dlib using real-world examples and datasets Free with your book: DRM-free PDF version + access to Packt's next-gen Reader* Key Features Familiarize yourself with data processing, performance measuring, and model selection using various C++ libraries Implement practical machine learning and deep learning techniques to build smart models Deploy machine learning models to work on mobile and embedded devices Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWritten by a seasoned software engineer with several years of industry experience, this book will teach you the basics of machine learning (ML) and show you how to use C++ libraries, along with helping you create supervised and unsupervised ML models. You’ll gain hands-on experience in tuning and optimizing a model for various use cases, enabling you to efficiently select models and measure performance. The chapters cover techniques such as product recommendations, ensemble learning, anomaly detection, sentiment analysis, and object recognition using modern C++ libraries. You’ll also learn how to overcome production and deployment challenges on mobile platforms, and see how the ONNX model format can help you accomplish these tasks. This edition is updated with key topics such as sentiment analysis implementation using transfer learning and transformer-based models, with tracking and visualizing ML experiments with MLflow. An additional section shows how to use Optuna for hyperparameter selection. The section on model deployment into mobile platform includes a detailed explanation of real-time object detection for Android with C++. By the end of this C++ book, you’ll have real-world machine learning and C++ knowledge, as well as the skills to use C++ to build powerful ML systems. *Email sign-up and proof of purchase requiredWhat you will learn Employ key machine learning algorithms using various C++ libraries Load and pre-process different data types to suitable C++ data structures Find out how to identify the best parameters for a machine learning model Use anomaly detection for filtering user data Apply collaborative filtering to manage dynamic user preferences Utilize C++ libraries and APIs to manage model structures and parameters Implement C++ code for object detection using a modern neural network Who this book is for This book is for beginners looking to explore machine learning algorithms and techniques using C++. This book is also valuable for data analysts, scientists, and developers who want to implement machine learning models in production. Working knowledge of C++ is needed to make the most of this book.
Deep Learning Research Applications For Natural Language Processing
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Author : Ashok Kumar, L.
language : en
Publisher: IGI Global
Release Date : 2022-12-09
Deep Learning Research Applications For Natural Language Processing written by Ashok Kumar, L. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-09 with Computers categories.
Humans have the most advanced method of communication, which is known as natural language. While humans can use computers to send voice and text messages to each other, computers do not innately know how to process natural language. In recent years, deep learning has primarily transformed the perspectives of a variety of fields in artificial intelligence (AI), including speech, vision, and natural language processing (NLP). The extensive success of deep learning in a wide variety of applications has served as a benchmark for the many downstream tasks in AI. The field of computer vision has taken great leaps in recent years and surpassed humans in tasks related to detecting and labeling objects thanks to advances in deep learning and neural networks. Deep Learning Research Applications for Natural Language Processing explains the concepts and state-of-the-art research in the fields of NLP, speech, and computer vision. It provides insights into using the tools and libraries in Python for real-world applications. Covering topics such as deep learning algorithms, neural networks, and advanced prediction, this premier reference source is an excellent resource for computational linguists, software engineers, IT managers, computer scientists, students and faculty of higher education, libraries, researchers, and academicians.
Encyclopedia Of Data Science And Machine Learning
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Author : Wang, John
language : en
Publisher: IGI Global
Release Date : 2023-01-20
Encyclopedia Of Data Science And Machine Learning written by Wang, John and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-20 with Computers categories.
Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.
Machine Learning
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Author : Dr. Gowthami S
language : en
Publisher: RK Publication
Release Date : 2025-06-17
Machine Learning written by Dr. Gowthami S and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-17 with Computers categories.
This book offers a comprehensive introduction to Machine Learning, covering fundamental concepts, algorithms, and practical applications. Designed for students, researchers, and professionals, it explores supervised, unsupervised, and reinforcement learning with real-world use cases. Emphasis is placed on model evaluation, optimization, and ethical AI practices in modern data-driven environments.
Mastering Large Language Models With Python Unleash The Power Of Advanced Natural Language Processing For Enterprise Innovation And Efficiency Using Large Language Models Llms With Python
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Author : Raj Arun
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2024-04-12
Mastering Large Language Models With Python Unleash The Power Of Advanced Natural Language Processing For Enterprise Innovation And Efficiency Using Large Language Models Llms With Python written by Raj Arun and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-12 with Computers categories.
A Comprehensive Guide to Leverage Generative AI in the Modern Enterprise Key Features● Gain a comprehensive understanding of LLMs within the framework of Generative AI, from foundational concepts to advanced applications. ● Dive into practical exercises and real-world applications, accompanied by detailed code walkthroughs in Python. ● Explore LLMOps with a dedicated focus on ensuring trustworthy AI and best practices for deploying, managing, and maintaining LLMs in enterprise settings. Book Description “Mastering Large Language Models with Python” is an indispensable resource that offers a comprehensive exploration of Large Language Models (LLMs), providing the essential knowledge to leverage these transformative AI models effectively. From unraveling the intricacies of LLM architecture to practical applications like code generation and AI-driven recommendation systems, readers will gain valuable insights into implementing LLMs in diverse projects. Covering both open-source and proprietary LLMs, the book delves into foundational concepts and advanced techniques, empowering professionals to harness the full potential of these models. Detailed discussions on quantization techniques for efficient deployment, operational strategies with LLMOps, and ethical considerations ensure a well-rounded understanding of LLM implementation. Through real-world case studies, code snippets, and practical examples, readers will navigate the complexities of LLMs with confidence, paving the way for innovative solutions and organizational growth. Whether you seek to deepen your understanding, drive impactful applications, or lead AI-driven initiatives, this book equips you with the tools and insights needed to excel in the dynamic landscape of artificial intelligence. What you will learn ● In-depth study of LLM architecture and its versatile applications across industries. ● Harness open-source and proprietary LLMs to craft innovative solutions. ● Implement LLM APIs for a wide range of tasks spanning natural language processing, audio analysis, and visual recognition. ● Optimize LLM deployment through techniques such as quantization and operational strategies like LLMOps, ensuring efficient and scalable model usage. Table of Contents 1. The Basics of Large Language Models and Their Applications 2. Demystifying Open-Source Large Language Models 3. Closed-Source Large Language Models 4. LLM APIs for Various Large Language Model Tasks 5. Integrating Cohere API in Google Sheets 6. Dynamic Movie Recommendation Engine Using LLMs 7. Document-and Web-based QA Bots with Large Language Models 8. LLM Quantization Techniques and Implementation 9. Fine-tuning and Evaluation of LLMs 10. Recipes for Fine-Tuning and Evaluating LLMs 11. LLMOps - Operationalizing LLMs at Scale 12. Implementing LLMOps in Practice Using MLflow on Databricks 13. Mastering the Art of Prompt Engineering 14. Prompt Engineering Essentials and Design Patterns 15. Ethical Considerations and Regulatory Frameworks for LLMs 16. Towards Trustworthy Generative AI (A Novel Framework Inspired by Symbolic Reasoning) Index
Data Science On Aws
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Author : Chris Fregly
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-04-07
Data Science On Aws written by Chris Fregly and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-07 with Computers categories.
With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level up your skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more
Machine Learning Engineering With Mlflow
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Author : Natu Lauchande
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-08-27
Machine Learning Engineering With Mlflow written by Natu Lauchande 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 2021-08-27 with Computers categories.
Get up and running, and productive in no time with MLflow using the most effective machine learning engineering approach Key FeaturesExplore machine learning workflows for stating ML problems in a concise and clear manner using MLflowUse MLflow to iteratively develop a ML model and manage it Discover and work with the features available in MLflow to seamlessly take a model from the development phase to a production environmentBook Description MLflow is a platform for the machine learning life cycle that enables structured development and iteration of machine learning models and a seamless transition into scalable production environments. This book will take you through the different features of MLflow and how you can implement them in your ML project. You will begin by framing an ML problem and then transform your solution with MLflow, adding a workbench environment, training infrastructure, data management, model management, experimentation, and state-of-the-art ML deployment techniques on the cloud and premises. The book also explores techniques to scale up your workflow as well as performance monitoring techniques. As you progress, you'll discover how to create an operational dashboard to manage machine learning systems. Later, you will learn how you can use MLflow in the AutoML, anomaly detection, and deep learning context with the help of use cases. In addition to this, you will understand how to use machine learning platforms for local development as well as for cloud and managed environments. This book will also show you how to use MLflow in non-Python-based languages such as R and Java, along with covering approaches to extend MLflow with Plugins. By the end of this machine learning book, you will be able to produce and deploy reliable machine learning algorithms using MLflow in multiple environments. What you will learnDevelop your machine learning project locally with MLflow's different featuresSet up a centralized MLflow tracking server to manage multiple MLflow experimentsCreate a model life cycle with MLflow by creating custom modelsUse feature streams to log model results with MLflowDevelop the complete training pipeline infrastructure using MLflow featuresSet up an inference-based API pipeline and batch pipeline in MLflowScale large volumes of data by integrating MLflow with high-performance big data librariesWho this book is for This book is for data scientists, machine learning engineers, and data engineers who want to gain hands-on machine learning engineering experience and learn how they can manage an end-to-end machine learning life cycle with the help of MLflow. Intermediate-level knowledge of the Python programming language is expected.
Mlflow Unleashed
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Author : Nova Trex
language : en
Publisher: Independently Published
Release Date : 2025-08-05
Mlflow Unleashed written by Nova Trex and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-05 with Computers categories.
"MLflow Unleashed: Real-World MLOps Workflows" is the definitive guide for machine learning engineers, data scientists, and technical leaders seeking to master the orchestration of robust, end-to-end MLOps solutions with MLflow at their core. The book opens with a forward-thinking exploration of modern MLOps challenges, thoughtfully contrasting them with traditional DevOps, and meticulously introduces MLflow's architecture-from experiment tracking and model management to registry and plugin extensibility. Readers are empowered with practical decision frameworks, advanced integration strategies for hybrid and cloud environments, and best practices for embedding MLflow seamlessly within an enterprise's data and machine learning ecosystem. Through detailed chapters on architecting resilient MLflow platforms, securing deployments, and implementing operational excellence, the book illuminates the path to scalable, production-grade workflows. It dives deep into automating experiment management, ensuring governance and compliance, and scaling collaboration across teams. Special attention is given to advanced reproducibility, lineage, and automation techniques using CI/CD, Kubernetes-native patterns, GitOps workflows, and infrastructure-as-code, ensuring repeatability and auditability in even the most complex environments. Readers will also uncover actionable approaches for model deployment, monitoring, and lifecycle management-including batch, real-time, and edge serving, as well as sophisticated rollback and canary release strategies. "MLflow Unleashed" culminates in expert guidance for extending MLflow to meet diverse organizational needs, from custom plugins and horizontal scaling to cost optimization and responsible AI practices. The book provides comprehensive insights on monitoring, observability, bias mitigation, and regulatory compliance, equipping practitioners to build accountable and future-proof AI systems. Both a practical handbook and a strategic reference, this work is essential for anyone committed to scaling machine learning operations with rigor, efficiency, and innovation in the real world.
Mlflow In Practice
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-14
Mlflow In Practice written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-14 with Computers categories.
"MLflow in Practice" "MLflow in Practice" is a comprehensive guide for data scientists, ML engineers, and enterprise practitioners seeking to harness the full power of MLflow in modern MLOps workflows. The book opens with a thorough exploration of MLflow’s core components—including Experiment Tracking, Projects, Models, and Model Registry—demystifying its architecture, deployment patterns, and seamless integration with leading platforms like Databricks, AzureML, Kubeflow, and Airflow. Readers gain valuable insights into positioning MLflow within the broader MLOps ecosystem, choosing between open source and enterprise offerings, and implementing robust security and governance practices from the outset. Delving deep into practical implementation, the book provides actionable best practices for managing experiments, logging and visualizing runs, packaging reproducible ML projects, and orchestrating scalable deployment pipelines. Advanced chapters address complex scenarios such as distributed experimentation, hybrid and multi-cloud deployments, model lifecycle management, automated retraining, and CI/CD integration. Coverage extends to securing sensitive data, ensuring compliance with industry regulations, and developing enterprise-ready ML systems with full traceability, auditability, and disaster recovery. Enriched with real-world case studies and forward-looking insights, "MLflow in Practice" showcases MLflow’s transformative role across diverse domains—from regulated enterprise environments and academic research to edge IoT and AI startups. Readers will not only learn how to deploy, monitor, and optimize ML models in production, but also stay ahead of emerging trends in generative AI, open standards, and collaborative experimentation. Whether you are modernizing machine learning operations or scaling ML workflows globally, this book equips you with the strategies, patterns, and technical know-how to maximize impact with MLflow.