Mastering Mlops Architecture From Code To Deployment
DOWNLOAD
Download Mastering Mlops Architecture From Code To Deployment PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Mastering Mlops Architecture From Code To Deployment book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Mastering Mlops Architecture From Code To Deployment
DOWNLOAD
Author : Raman Jhajj
language : en
Publisher: BPB Publications
Release Date : 2023-12-12
Mastering Mlops Architecture From Code To Deployment written by Raman Jhajj and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-12 with Computers categories.
Harness the power of MLOps for managing real time machine learning project cycle KEY FEATURES ● Comprehensive coverage of MLOps concepts, architecture, tools and techniques. ● Practical focus on building end-to-end ML Systems for Continual Learning with MLOps. ● Actionable insights on CI/CD, monitoring, continual model training and automated retraining. DESCRIPTION MLOps, a combination of DevOps, data engineering, and machine learning, is crucial for delivering high-quality machine learning results due to the dynamic nature of machine learning data. This book delves into MLOps, covering its core concepts, components, and architecture, demonstrating how MLOps fosters robust and continuously improving machine learning systems. By covering the end-to-end machine learning pipeline from data to deployment, the book helps readers implement MLOps workflows. It discusses techniques like feature engineering, model development, A/B testing, and canary deployments. The book equips readers with knowledge of MLOps tools and infrastructure for tasks like model tracking, model governance, metadata management, and pipeline orchestration. Monitoring and maintenance processes to detect model degradation are covered in depth. Readers can gain skills to build efficient CI/CD pipelines, deploy models faster, and make their ML systems more reliable, robust and production-ready. Overall, the book is an indispensable guide to MLOps and its applications for delivering business value through continuous machine learning and AI. WHAT YOU WILL LEARN ● Architect robust MLOps infrastructure with components like feature stores. ● Leverage MLOps tools like model registries, metadata stores, pipelines. ● Build CI/CD workflows to deploy models faster and continually. ● Monitor and maintain models in production to detect degradation. ● Create automated workflows for retraining and updating models in production. WHO THIS BOOK IS FOR Machine learning specialists, data scientists, DevOps professionals, software development teams, and all those who want to adopt the DevOps approach in their agile machine learning experiments and applications. Prior knowledge of machine learning and Python programming is desired. TABLE OF CONTENTS 1. Getting Started with MLOps 2. MLOps Architecture and Components 3. MLOps Infrastructure and Tools 4. What are Machine Learning Systems? 5. Data Preparation and Model Development 6. Model Deployment and Serving 7. Continuous Delivery of Machine Learning Models 8. Continual Learning 9. Continuous Monitoring, Logging, and Maintenance
Cyber Forensics Up And Running
DOWNLOAD
Author : Tarun Vashishth
language : en
Publisher: BPB Publications
Release Date : 2023-12-12
Cyber Forensics Up And Running written by Tarun Vashishth and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-12 with Computers categories.
Empowering you to investigate, analyze, and secure the digital realm KEY FEATURES ● Comprehensive coverage of all digital forensics concepts. ● Real-world case studies and examples to illustrate techniques. ● Step-by-step instructions for setting up and using essential forensic tools. ● In-depth exploration of volatile and non-volatile data analysis. DESCRIPTION Digital forensics is the art and science of extracting the hidden truth and this book is your hands-on companion, bringing the world of digital forensics to life. Starting with the core principles of digital forensics, the book explores the significance of various case types, the interconnectedness of the field with cybersecurity, and the ever-expanding digital world's challenges. As you progress, you will explore data acquisition, image formats, digital evidence preservation, file carving, metadata extraction, and the practical use of essential forensic tools like HxD, The Sleuth Kit, Autopsy, Volatility, and PowerForensics. The book offers step-by-step instructions, real-world case studies, and practical examples, ensuring that beginners can confidently set up and use forensic tools. Experienced professionals, on the other hand, will find advanced insights into memory analysis, network forensics, anti-forensic techniques, and more. This book empowers you to become a digital detective, capable of uncovering data secrets, investigating networks, exploring volatile and non-volatile evidence, and understanding the intricacies of modern browsers and emails. WHAT YOU WILL LEARN ● Learn how to set up and use digital forensic tools, including virtual environments. ● Learn about live forensics, incident response, and timeline examination. ● In-depth exploration of Windows Registry and USBs. ● Network forensics, PCAPs, and malware scenarios. ● Memory forensics, malware detection, and file carving. ● Advance tools like PowerForensics and Autopsy. WHO THIS BOOK IS FOR Whether you are a tech-savvy detective, a curious student, or a seasoned cybersecurity pro seeking to amplify your skillset. Network admins, law enforcement officers, incident responders, aspiring analysts, and even legal professionals will find invaluable tools and techniques within these pages. TABLE OF CONTENTS 1. Introduction to Essential Concepts of Digital Forensics 2. Digital Forensics Lab Setup 3. Data Collection: Volatile and Non-Volatile 4. Forensics Analysis: Live Response 5. File System and Log Analysis 6. Windows Registry and Artifacts 7. Network Data Collection and Analysis 8. Memory Forensics: Techniques and Tools 9. Browser and Email Forensics 10. Advanced Forensics Tools, Commands and Methods 11. Anti-Digital Forensics Techniques and Methods
Mastering Azure Machine Learning
DOWNLOAD
Author : Christoph Körner
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-04-30
Mastering Azure Machine Learning written by Christoph Körner 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 2020-04-30 with Computers categories.
Master expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using TensorFlow, Spark, and Kubernetes Key FeaturesMake sense of data on the cloud by implementing advanced analyticsTrain and optimize advanced deep learning models efficiently on Spark using Azure DatabricksDeploy machine learning models for batch and real-time scoring with Azure Kubernetes Service (AKS)Book Description The increase being seen in data volume today requires distributed systems, powerful algorithms, and scalable cloud infrastructure to compute insights and train and deploy machine learning (ML) models. This book will help you improve your knowledge of building ML models using Azure and end-to-end ML pipelines on the cloud. The book starts with an overview of an end-to-end ML project and a guide on how to choose the right Azure service for different ML tasks. It then focuses on Azure Machine Learning and takes you through the process of data experimentation, data preparation, and feature engineering using Azure Machine Learning and Python. You'll learn advanced feature extraction techniques using natural language processing (NLP), classical ML techniques, and the secrets of both a great recommendation engine and a performant computer vision model using deep learning methods. You'll also explore how to train, optimize, and tune models using Azure Automated Machine Learning and HyperDrive, and perform distributed training on Azure. Then, you'll learn different deployment and monitoring techniques using Azure Kubernetes Services with Azure Machine Learning, along with the basics of MLOps—DevOps for ML to automate your ML process as CI/CD pipeline. By the end of this book, you'll have mastered Azure Machine Learning and be able to confidently design, build and operate scalable ML pipelines in Azure. What you will learnSetup your Azure Machine Learning workspace for data experimentation and visualizationPerform ETL, data preparation, and feature extraction using Azure best practicesImplement advanced feature extraction using NLP and word embeddingsTrain gradient boosted tree-ensembles, recommendation engines and deep neural networks on Azure Machine LearningUse hyperparameter tuning and Azure Automated Machine Learning to optimize your ML modelsEmploy distributed ML on GPU clusters using Horovod in Azure Machine LearningDeploy, operate and manage your ML models at scaleAutomated your end-to-end ML process as CI/CD pipelines for MLOpsWho this book is for This machine learning book is for data professionals, data analysts, data engineers, data scientists, or machine learning developers who want to master scalable cloud-based machine learning architectures in Azure. This book will help you use advanced Azure services to build intelligent machine learning applications. A basic understanding of Python and working knowledge of machine learning are mandatory.
Mastering Data Engineering And Analytics With Databricks A Hands On Guide To Build Scalable Pipelines Using Databricks Delta Lake And Mlflow
DOWNLOAD
Author : Manoj Kumar
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2024-09-30
Mastering Data Engineering And Analytics With Databricks A Hands On Guide To Build Scalable Pipelines Using Databricks Delta Lake And Mlflow written by Manoj Kumar 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 2024-09-30 with Computers categories.
Master Databricks to Transform Data into Strategic Insights for Tomorrow’s Business Challenges Key Features● Combines theory with practical steps to master Databricks, Delta Lake, and MLflow.● Real-world examples from FMCG and CPG sectors demonstrate Databricks in action.● Covers real-time data processing, ML integration, and CI/CD for scalable pipelines.● Offers proven strategies to optimize workflows and avoid common pitfalls. Book DescriptionIn today’s data-driven world, mastering data engineering is crucial for driving innovation and delivering real business impact. Databricks is one of the most powerful platforms which unifies data, analytics and AI requirements of numerous organizations worldwide. Mastering Data Engineering and Analytics with Databricks goes beyond the basics, offering a hands-on, practical approach tailored for professionals eager to excel in the evolving landscape of data engineering and analytics. This book uniquely blends foundational knowledge with advanced applications, equipping readers with the expertise to build, optimize, and scale data pipelines that meet real-world business needs. With a focus on actionable learning, it delves into complex workflows, including real-time data processing, advanced optimization with Delta Lake, and seamless ML integration with MLflow—skills critical for today’s data professionals. Drawing from real-world case studies in FMCG and CPG industries, this book not only teaches you how to implement Databricks solutions but also provides strategic insights into tackling industry-specific challenges. From setting up your environment to deploying CI/CD pipelines, you'll gain a competitive edge by mastering techniques that are directly applicable to your organization’s data strategy. By the end, you’ll not just understand Databricks—you’ll command it, positioning yourself as a leader in the data engineering space. What you will learn● Design and implement scalable, high-performance data pipelines using Databricks for various business use cases.● Optimize query performance and efficiently manage cloud resources for cost-effective data processing.● Seamlessly integrate machine learning models into your data engineering workflows for smarter automation.● Build and deploy real-time data processing solutions for timely and actionable insights.● Develop reliable and fault-tolerant Delta Lake architectures to support efficient data lakes at scale. Table of ContentsSECTION 11. Introducing Data Engineering with Databricks2. Setting Up a Databricks Environment for Data Engineering3. Working with Databricks Utilities and ClustersSECTION 24. Extracting and Loading Data Using Databricks5. Transforming Data with Databricks6. Handling Streaming Data with Databricks7. Creating Delta Live Tables8. Data Partitioning and Shuffling9. Performance Tuning and Best Practices10. Workflow Management11. Databricks SQL Warehouse12. Data Storage and Unity Catalog13. Monitoring Databricks Clusters and Jobs14. Production Deployment Strategies15. Maintaining Data Pipelines in Production16. Managing Data Security and Governance17. Real-World Data Engineering Use Cases with Databricks18. AI and ML Essentials19. Integrating Databricks with External Tools Index
Mastering Pytorch
DOWNLOAD
Author : Ashish Ranjan Jha
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-02-12
Mastering Pytorch written by Ashish Ranjan Jha 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 2021-02-12 with Computers categories.
Master advanced techniques and algorithms for deep learning with PyTorch using real-world examples Key Features Understand how to use PyTorch 1.x to build advanced neural network models Learn to perform a wide range of tasks by implementing deep learning algorithms and techniques Gain expertise in domains such as computer vision, NLP, Deep RL, Explainable AI, and much more Book DescriptionDeep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models. The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) architectures and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation using generative models and explore the world of generative adversarial networks (GANs). You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai. By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.What you will learn Implement text and music generating models using PyTorch Build a deep Q-network (DQN) model in PyTorch Export universal PyTorch models using Open Neural Network Exchange (ONNX) Become well-versed with rapid prototyping using PyTorch with fast.ai Perform neural architecture search effectively using AutoML Easily interpret machine learning (ML) models written in PyTorch using Captum Design ResNets, LSTMs, Transformers, and more using PyTorch Find out how to use PyTorch for distributed training using the torch.distributed API Who this book is for This book is for data scientists, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning paradigms using PyTorch 1.x. Working knowledge of deep learning with Python programming is required.
Machine Learning Design Patterns
DOWNLOAD
Author : Valliappa Lakshmanan
language : en
Publisher: O'Reilly Media
Release Date : 2020-10-15
Machine Learning Design Patterns written by Valliappa Lakshmanan and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-15 with Computers categories.
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly
Machine Learning Engineering With Python
DOWNLOAD
Author : Andrew P. McMahon
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-11-05
Machine Learning Engineering With Python written by Andrew P. McMahon 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 2021-11-05 with Computers categories.
Supercharge the value of your machine learning models by building scalable and robust solutions that can serve them in production environments Key Features Explore hyperparameter optimization and model management tools Learn object-oriented programming and functional programming in Python to build your own ML libraries and packages Explore key ML engineering patterns like microservices and the Extract Transform Machine Learn (ETML) pattern with use cases Book DescriptionMachine learning engineering is a thriving discipline at the interface of software development and machine learning. This book will help developers working with machine learning and Python to put their knowledge to work and create high-quality machine learning products and services. Machine Learning Engineering with Python takes a hands-on approach to help you get to grips with essential technical concepts, implementation patterns, and development methodologies to have you up and running in no time. You'll begin by understanding key steps of the machine learning development life cycle before moving on to practical illustrations and getting to grips with building and deploying robust machine learning solutions. As you advance, you'll explore how to create your own toolsets for training and deployment across all your projects in a consistent way. The book will also help you get hands-on with deployment architectures and discover methods for scaling up your solutions while building a solid understanding of how to use cloud-based tools effectively. Finally, you'll work through examples to help you solve typical business problems. By the end of this book, you'll be able to build end-to-end machine learning services using a variety of techniques and design your own processes for consistently performant machine learning engineering.What you will learn Find out what an effective ML engineering process looks like Uncover options for automating training and deployment and learn how to use them Discover how to build your own wrapper libraries for encapsulating your data science and machine learning logic and solutions Understand what aspects of software engineering you can bring to machine learning Gain insights into adapting software engineering for machine learning using appropriate cloud technologies Perform hyperparameter tuning in a relatively automated way Who this book is for This book is for machine learning engineers, data scientists, and software developers who want to build robust software solutions with machine learning components. If you're someone who manages or wants to understand the production life cycle of these systems, you'll find this book useful. Intermediate-level knowledge of Python is necessary.
Building Machine Learning Pipelines
DOWNLOAD
Author : Hannes Hapke
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2020-07-13
Building Machine Learning Pipelines written by Hannes Hapke 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 2020-07-13 with Computers categories.
Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques
Learning Elastic Stack 7 0
DOWNLOAD
Author : Pranav Shukla
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-05-31
Learning Elastic Stack 7 0 written by Pranav Shukla 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 2019-05-31 with Computers categories.
A beginner's guide to storing, managing, and analyzing data with the updated features of Elastic 7.0 Key FeaturesGain access to new features and updates introduced in Elastic Stack 7.0Grasp the fundamentals of Elastic Stack including Elasticsearch, Logstash, and KibanaExplore useful tips for using Elastic Cloud and deploying Elastic Stack in production environmentsBook Description The Elastic Stack is a powerful combination of tools for techniques such as distributed search, analytics, logging, and visualization of data. Elastic Stack 7.0 encompasses new features and capabilities that will enable you to find unique insights into analytics using these techniques. This book will give you a fundamental understanding of what the stack is all about, and help you use it efficiently to build powerful real-time data processing applications. The first few sections of the book will help you understand how to set up the stack by installing tools, and exploring their basic configurations. You’ll then get up to speed with using Elasticsearch for distributed searching and analytics, Logstash for logging, and Kibana for data visualization. As you work through the book, you will discover the technique of creating custom plugins using Kibana and Beats. This is followed by coverage of the Elastic X-Pack, a useful extension for effective security and monitoring. You’ll also find helpful tips on how to use Elastic Cloud and deploy Elastic Stack in production environments. By the end of this book, you’ll be well versed with the fundamental Elastic Stack functionalities and the role of each component in the stack to solve different data processing problems. What you will learnInstall and configure an Elasticsearch architectureSolve the full-text search problem with ElasticsearchDiscover powerful analytics capabilities through aggregations using ElasticsearchBuild a data pipeline to transfer data from a variety of sources into Elasticsearch for analysisCreate interactive dashboards for effective storytelling with your data using KibanaLearn how to secure, monitor and use Elastic Stack’s alerting and reporting capabilitiesTake applications to an on-premise or cloud-based production environment with Elastic StackWho this book is for This book is for entry-level data professionals, software engineers, e-commerce developers, and full-stack developers who want to learn about Elastic Stack and how the real-time processing and search engine works for business analytics and enterprise search applications. Previous experience with Elastic Stack is not required, however knowledge of data warehousing and database concepts will be helpful.
Mastering Machine Learning For Penetration Testing
DOWNLOAD
Author : Chiheb Chebbi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-06-27
Mastering Machine Learning For Penetration Testing written by Chiheb Chebbi 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 2018-06-27 with Language Arts & Disciplines categories.
Become a master at penetration testing using machine learning with Python Key Features Identify ambiguities and breach intelligent security systems Perform unique cyber attacks to breach robust systems Learn to leverage machine learning algorithms Book Description Cyber security is crucial for both businesses and individuals. As systems are getting smarter, we now see machine learning interrupting computer security. With the adoption of machine learning in upcoming security products, it’s important for pentesters and security researchers to understand how these systems work, and to breach them for testing purposes. This book begins with the basics of machine learning and the algorithms used to build robust systems. Once you’ve gained a fair understanding of how security products leverage machine learning, you'll dive into the core concepts of breaching such systems. Through practical use cases, you’ll see how to find loopholes and surpass a self-learning security system. As you make your way through the chapters, you’ll focus on topics such as network intrusion detection and AV and IDS evasion. We’ll also cover the best practices when identifying ambiguities, and extensive techniques to breach an intelligent system. By the end of this book, you will be well-versed with identifying loopholes in a self-learning security system and will be able to efficiently breach a machine learning system. What you will learn Take an in-depth look at machine learning Get to know natural language processing (NLP) Understand malware feature engineering Build generative adversarial networks using Python libraries Work on threat hunting with machine learning and the ELK stack Explore the best practices for machine learning Who this book is for This book is for pen testers and security professionals who are interested in learning techniques to break an intelligent security system. Basic knowledge of Python is needed, but no prior knowledge of machine learning is necessary.