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ENbook.pl

1. Introduction to Machine Learning 2. Oracle and Machine Learning3 Oracle Machine Learning for SQL4. Oracle Autonomous Database for Machine Learning5. Running Oracle Machine Learning with Autonomous Database6 Building Machine Learning Models with OML Notebooks7. Oracle Analytics Cloud8. Delivery and Automation Pipeline in Machine Learning9. ML Deployment Pipeline Using Oracle Machine Learning10. Building Reproducible ML Pipelines Using Oracle Machine Learningbr

Machine Learning for Oracle Database Professionals | Helskyaho Heli | Twarda

Chapter 1 Getting Started Data Analysis and Feature Engineering Chapter Goal Establish the premise of the problem we want to solve with machine learning. Analyze several data sets and process them. No of pages - 30 pages Sub - Topics 1. Premise 4. Data analysis 5. Feature engineering Chapter 2 Building a Machine Learning Model Chapter Goal Build a machine learning model on a data set  several data sets that we processed the data for in chapter 4.No of pages - 40 pagesSub - Topics 1. Building the model 2. Training and testing the model 3. Validation and optimizing Chapter 3 What is MLOps Chapter Goal Introduce the reader to MLOps, various stages of automation in MLOps setups, automation with pipeline, and to CICD and CD Deployment. Pipelines for source repo to deployment, prediction services, performance monitoring, etc Continuous Integration source repo updated with new models, and Continuous Delivery new models deployed. No of pages - 40 pages Sub -Topics 1. What is MLOps 2. MLOps set

Beginning Mlops with Mlflow | Alla Sridhar | Twarda

pstrongGet up and running with machine learning life cycle management and implement MLOps in your organizationstrongppbrppstrongKey Features strongpulliBecome well-versed with MLOps techniques to monitor the quality of machine learning models in productionliliExplore a monitoring framework for ML models in production and learn about end-to-end traceability for deployed modelsliliPerform CICD to automate new implementations in ML pipelinesliulpbrppstrongBook Description strongppMLOps is a systematic approach to building, deploying, and monitoring machine learning ML solutions. It is an engineering discipline that can be applied to various industries and use cases. This book presents comprehensive insights into MLOps coupled with real-world examples to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production.ppbrppThe book begins by familiarizing you with the MLOps workflow so you can start writing programs 

Engineering MLOps | Raj Emmanuel | Twarda

pstrongUnderstand the complexities of modern-day data engineering platforms and explore strategies to deal with them with the help of use case scenarios led by an industry expert in big datastrongppbrppstrongKey Features strongpulliBecome well-versed with the core concepts of Apache Spark and Delta Lake for building data platformsliliLearn how to ingest, process, and analyze data that can be later used for training machine learning modelsliliUnderstand how to operationalize data models in production using curated dataliulpbrppstrongBook Description strongppIn the world of ever-changing data and ever-evolving schemas, it is important to build data pipelines that can auto-adjust to changes. This book will help you build scalable data platforms that managers, data scientists, and data analysts can rely on.ppbrppStarting with an introduction to data engineering, along with its key concepts and architectures, this book will show you how to use Microsoft Azure Cloud services effectively for 

Data Engineering with Apache Spark, Delta Lake, and Lakehouse | Kukreja Manoj | Twarda

pCompanies 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. p 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. ul liUnderstand the steps to build a machine learning pipeline liBuild your pipeline using components from TensorFlow Extended liOrchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines liWork with data using TensorFlow

Building Machine Learning Pipelines | Hapke Hannes | Twarda

pstrongTransform your machine learning projects into successful deployments with this practical guide on how to build and scale solutions that solve real-world problemsstrongppbrppstrongIncludes a new chapter on generative AI and large language models LLMs and building a pipeline that leverages LLMs using LangChainstrongppbrppstrongKey Features strongppbrpulliThis second edition delves deeper into key machine learning topics, CICD, and system designliliExplore core MLOps practices, such as model management and performance monitoringliliBuild end-to-end examples of deployable ML microservices and pipelines using AWS and open-source toolsliulpbrppstrongBook Description strongppbrppMachine Learning Engineering with Python, 2nd Edition, is the practical guide that MLOps and ML engineers need to build robust solutions to solve real-world problems, providing you with the skills and knowledge you need to stay ahead in this rapidly evolving field.ppbrppThe book takes a hands-on, examples-focus

Machine Learning Engineering with Python | Second Edition | McMahon Andrew | Twarda

pWith the rapid growth of big data and AI, organizations are quickly building data products and solutions in an ad-hoc manner. But as these data organizations mature, it's apparent that their analysis and machine learning models are only as reliable as the data they're built upon. The solution Delta Lake, an open-source format that enables building a lakehouse architecture on top of existing storage systems such as S3, ADLS, and GCS. p In this practical book, author Bennie Haelen shows data engineers, data scientists, and data analysts how to get Delta Lake and its unique features up and running. The ultimate goal of building data pipelines and applications is to query processed data and gain insights from it. You'll learn how the choice of storage solution determines the robustness and performance of the data pipeline, from raw data to insights. p With this book, you will ul liUse modern data management and data engineering techniques liUnderstand how ACID transactions bring reliabili

Delta Lake | Haelen Bennie | Twarda

pmlr3 is an award-winning ecosystem of R packages that have been developed to enable state-of-the-art machine learning capabilities in R. bApplied Machine Learning Using mlr3 in Rb gives an overview of flexible and robust machine learning methods, with an emphasis on how to implement them using mlr3 in R. It covers various key topics, including basic machine learning tasks, such as building and evaluating a predictive model hyperparameter tuning of machine learning approaches to obtain peak performance building machine learning pipelines that perform complex operations such as pre-processing followed by modelling followed by aggregation of predictions and extending the mlr3 ecosystem with custom learners, measures, or pipeline components.ppFeatures pul liIn-depth coverage of the mlr3 ecosystem for users and developersli liExplanation and illustration of basic and advanced machine learning conceptsli liReady to use code samples that can be adapted by the user for their applicationli liC

Applied Machine Learning Using mlr3 in R | Bischl Bernd | Paperback | Twarda

pMaster the new features in PySpark 3.1 to develop data-driven, intelligent applications. This updated edition covers topics ranging from building scalable machine learning models, to natural language processing, to recommender systems.p piMachine Learning with PySpark, Second Editioni begins with the fundamentals of Apache Spark, including the latest updates to the framework. Next, you will learn the full spectrum of traditional machine learning algorithm implementations, along with natural language processing and recommender systems. You'll gain familiarity with the critical process of selecting machine learning algorithms, data ingestion, and data processing to solve business problems. You'll see a demonstration of how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forests. You'll also learn how to automate the steps using Spark pipelines, followed by unsupervised models such as K-means and hierarchical clusteri

Machine Learning with PySpark | Singh Pramod | Twarda

pLearn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google Cloud Platform GCP. This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on GCP.ppThrough the course of this updated second edition, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by implementing these statistical and machine learning solutions in your own project on GCP, and discover how this platform provides a transformative and more collaborative way of doing data science.ppYou'll learn how to pulliEmploy best practices in building highly scalable data and ML pipelines on Google CloudliliAutomate and schedule data ingest using Cloud RunliliCreate and populate a dashboard in Data StudioliliBuild a real-time analytics pipeline using PubSub, Dataflow, and BigQueryliliConduct int

Data Science on the Google Cloud Platform | Lakshmanan Valliappa | Twarda

pstrongMaster expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using TensorFlow, Spark, and KubernetesstrongppstrongKey Featuresstrongp ul liMake sense of data on the cloud by implementing advanced analyticsli liTrain and optimize advanced deep learning models efficiently on Spark using Azure Databricksli liDeploy machine learning models for batch and real-time scoring with Azure Kubernetes Service AKSli ul pstrongBook Descriptionstrongp pThe 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.p pThe 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 

Mastering Azure Machine Learning | Waaijer Kaijisse | Twarda

pWith demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production. p Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs. p You'll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you ul liLearn the MLOps process, including its technological

Implementing Mlops in the Enterprise | Haviv Yaron | Twarda

pLeverage Kubernetes for the rapid adoption of emerging technologies. Kubernetes is the future of enterprise platform development and has become the most popular, and often considered the most robust, container orchestration system available today. This book focuses on platforming technologies that power the Internet of Things, Blockchain, Machine Learning, and the many layers of data and application management supporting them.p p emAdvanced Platform Development with Kubernetesem takes you through the process of building platforms with these in-demand capabilities. You'll progress through the development of Serverless, CICD integration, data processing pipelines, event queues, distributed query engines, modern data warehouses, data lakes, distributed object storage, indexing and analytics, data routing and transformation, query engines, and data sciencemachine learning environments. You'll also see how to implement and tie together numerous essential and trending technologies including

Advanced Platform Development with Kubernetes | Johnston Craig | Twarda

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pHow can music withstand the death and destruction brought on by war Global conflicts of the 20th century fundamentally transformed not only national boundaries, power relations, and global economies, but also the arts and culture of every nation involved. An important, unacknowledged aspect of these conflicts is that they have unique musical soundtracks. iMusic in World War IIi explores how music and sound took on radically different dimensions in the United States and Europe before, during, and after World War II. Additionally, the collection examines the impact of radio and film as the disseminators of the war's musical soundtrack. Contributors contend that the European and American soundtrack of World War II was largely one of escapism rather than the lofty, solemn, heroic, and celebratory mode of war music in the past. Furthermore, they explore the variety of experiences of populations forced from their homes and interned in civilian and POW camps in Europe and the United States, 

Music in World War II | Potter Pamela M. | Twarda

ENdisc.pl

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Spodnie męskie Lacoste Tracksuit Trousers Bottom XH9624-CCA – szare Męskie spodnie dresowe Lacoste Tracksuit Trousers Bottom. Zostały wykonane z wysokiej jakości materiału, który gwarantuje komfort użytkowania. Odzież posiada klasyczny krój z elastyczną, regulowaną za pomocą sznurka talią. Nogawki zakończone ściągaczem świetnie wyeksponują Twoje buty i zapewnią optymalne dopasowanie, a boczne kieszenie zmieszczą niezbędne drobiazgi. Męskie spodnie dresowe zadowolą fanów miejskiego stylu. Model ten świetnie dopełni męskie bluzy. W takim zestawie będziesz mógł wyruszyć na podbój miejskiej dżungli. Specyfikacja: - materiał: 84% bawełna, 16% poliester - klasyczny krój - elastyczna talia regulowana za pomocą sznurka - boczne kieszenie - nogawki zakończone ściągaczem - branding producentaPodmiot odpowiedzialny: Devanlay Polska Spółka z o. o. ul. Złota 59, 00-120 Warszawa, Polska