Programowanie

Machine Learning on Kubernetes. A practical handbook for building and using a complete open source machine learning platform on Kubernetes

Faisal Masood, Ross Brigoli

MLOps is an emerging field that aims to bring repeatability, automation, and standardization of the software engineering domain to data science and machine learning engineering. By implementing MLOps with Kubernetes, data scientists, IT professionals, and data engineers can collaborate and build machine learning solutions that deliver business value for their organization.You'll begin by understanding the different components of a machine learning project. Then, you'll design and build a practical end-to-end machine learning project using open source software. As you progress, you'll understand the basics of MLOps and the value it can bring to machine learning projects. You will also gain experience in building, configuring, and using an open source, containerized machine learning platform. In later chapters, you will prepare data, build and deploy machine learning models, and automate workflow tasks using the same platform. Finally, the exercises in this book will help you get hands-on experience in Kubernetes and open source tools, such as JupyterHub, MLflow, and Airflow.By the end of this book, you'll have learned how to effectively build, train, and deploy a machine learning model using the machine learning platform you built.

Machine learning, Python i data science. Wprowadzenie

Andreas Müller, Sarah Guido

Uczenie maszynowe kojarzy się z dużymi firmami i rozbudowanymi zespołami. Prawda jest taka, że obecnie można samodzielnie budować zaawansowane rozwiązania uczenia maszynowego i korzystać do woli z olbrzymich zasobów dostępnych danych. Trzeba tylko mieć pomysł i... trochę podstawowej wiedzy. Tymczasem większość opracowań na temat uczenia maszynowego i sztucznej inteligencji wymaga biegłości w zaawansowanej matematyce. Utrudnia to naukę tego zagadnienia, mimo że uczenie maszynowe jest coraz powszechniej stosowane w projektach badawczych i komercyjnych. Ta praktyczna książka ułatwi Ci rozpoczęcie wdrażania rozwiązań rzeczywistych problemów związanych z uczeniem maszynowym. Zawiera przystępne wprowadzenie do uczenia maszynowego i sztucznej inteligencji, a także sposoby wykorzystania Pythona i biblioteki scikit-learn, uwzględniające potrzeby badaczy i analityków danych oraz inżynierów pracujących nad aplikacjami komercyjnymi. Zagadnienia matematyczne ograniczono tu do niezbędnego minimum, zamiast tego skoncentrowano się na praktycznych aspektach algorytmów uczenia maszynowego. Dokładnie opisano, jak konkretnie można skorzystać z szerokiej gamy modeli zaimplementowanych w dostępnych bibliotekach. W książce między innymi: podstawowe informacje o uczeniu maszynowym najważniejsze algorytmy uczenia maszynowego przetwarzanie danych w uczeniu maszynowym ocena modelu i dostrajanie parametrów łańcuchy modeli i hermetyzacja przepływu pracy przetwarzanie danych tekstowych Python i uczenie maszynowe: programowanie do zadań specjalnych!

Machine learning, Python i data science. Wprowadzenie

Andreas Müller, Sarah Guido

Uczenie maszynowe kojarzy się z dużymi firmami i rozbudowanymi zespołami. Prawda jest taka, że obecnie można samodzielnie budować zaawansowane rozwiązania uczenia maszynowego i korzystać do woli z olbrzymich zasobów dostępnych danych. Trzeba tylko mieć pomysł i... trochę podstawowej wiedzy. Tymczasem większość opracowań na temat uczenia maszynowego i sztucznej inteligencji wymaga biegłości w zaawansowanej matematyce. Utrudnia to naukę tego zagadnienia, mimo że uczenie maszynowe jest coraz powszechniej stosowane w projektach badawczych i komercyjnych. Ta praktyczna książka ułatwi Ci rozpoczęcie wdrażania rozwiązań rzeczywistych problemów związanych z uczeniem maszynowym. Zawiera przystępne wprowadzenie do uczenia maszynowego i sztucznej inteligencji, a także sposoby wykorzystania Pythona i biblioteki scikit-learn, uwzględniające potrzeby badaczy i analityków danych oraz inżynierów pracujących nad aplikacjami komercyjnymi. Zagadnienia matematyczne ograniczono tu do niezbędnego minimum, zamiast tego skoncentrowano się na praktycznych aspektach algorytmów uczenia maszynowego. Dokładnie opisano, jak konkretnie można skorzystać z szerokiej gamy modeli zaimplementowanych w dostępnych bibliotekach. W książce między innymi: podstawowe informacje o uczeniu maszynowym najważniejsze algorytmy uczenia maszynowego przetwarzanie danych w uczeniu maszynowym ocena modelu i dostrajanie parametrów łańcuchy modeli i hermetyzacja przepływu pracy przetwarzanie danych tekstowych Python i uczenie maszynowe: programowanie do zadań specjalnych!

Machine Learning Security with Azure. Best practices for assessing, securing, and monitoring Azure Machine Learning workloads

Georgia Kalyva, George Kavvalakis

With AI and machine learning (ML) models gaining popularity and integrating into more and more applications, it is more important than ever to ensure that models perform accurately and are not vulnerable to cyberattacks. However, attacks can target your data or environment as well. This book will help you identify security risks and apply the best practices to protect your assets on multiple levels, from data and models to applications and infrastructure.This book begins by introducing what some common ML attacks are, how to identify your risks, and the industry standards and responsible AI principles you need to follow to gain an understanding of what you need to protect. Next, you will learn about the best practices to secure your assets. Starting with data protection and governance and then moving on to protect your infrastructure, you will gain insights into managing and securing your Azure ML workspace. This book introduces DevOps practices to automate your tasks securely and explains how to recover from ML attacks. Finally, you will learn how to set a security benchmark for your scenario and best practices to maintain and monitor your security posture.By the end of this book, you’ll be able to implement best practices to assess and secure your ML assets throughout the Azure Machine Learning life cycle.

Machine Learning Using TensorFlow Cookbook. Create powerful machine learning algorithms with TensorFlow

Alexia Audevart, Konrad Banachewicz, Luca Massaron

The independent recipes in Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data. Dive into recipes on training models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning - each using Google’s machine learning library, TensorFlow.This cookbook covers the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You’ll discover real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and regression.Explore the practical applications of a variety of deep learning architectures, such as recurrent neural networks and Transformers, and see how they can be used to solve computer vision and natural language processing (NLP) problems.With the help of this book, you will be proficient in using TensorFlow, understand deep learning from the basics, and be able to implement machine learning algorithms in real-world scenarios.

Machine Learning with Amazon SageMaker Cookbook. 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments

Joshua Arvin Lat

Amazon SageMaker is a fully managed machine learning (ML) service that helps data scientists and ML practitioners manage ML experiments. In this book, you'll use the different capabilities and features of Amazon SageMaker to solve relevant data science and ML problems.This step-by-step guide features 80 proven recipes designed to give you the hands-on machine learning experience needed to contribute to real-world experiments and projects. You'll cover the algorithms and techniques that are commonly used when training and deploying NLP, time series forecasting, and computer vision models to solve ML problems. You'll explore various solutions for working with deep learning libraries and frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers in Amazon SageMaker. You'll also learn how to use SageMaker Clarify, SageMaker Model Monitor, SageMaker Debugger, and SageMaker Experiments to debug, manage, and monitor multiple ML experiments and deployments. Moreover, you'll have a better understanding of how SageMaker Feature Store, Autopilot, and Pipelines can meet the specific needs of data science teams.By the end of this book, you'll be able to combine the different solutions you've learned as building blocks to solve real-world ML problems.

Machine Learning with Amazon SageMaker Cookbook. 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments

Joshua Arvin Lat

Amazon SageMaker is a fully managed machine learning (ML) service that helps data scientists and ML practitioners manage ML experiments. In this book, you'll use the different capabilities and features of Amazon SageMaker to solve relevant data science and ML problems.This step-by-step guide features 80 proven recipes designed to give you the hands-on machine learning experience needed to contribute to real-world experiments and projects. You'll cover the algorithms and techniques that are commonly used when training and deploying NLP, time series forecasting, and computer vision models to solve ML problems. You'll explore various solutions for working with deep learning libraries and frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers in Amazon SageMaker. You'll also learn how to use SageMaker Clarify, SageMaker Model Monitor, SageMaker Debugger, and SageMaker Experiments to debug, manage, and monitor multiple ML experiments and deployments. Moreover, you'll have a better understanding of how SageMaker Feature Store, Autopilot, and Pipelines can meet the specific needs of data science teams.By the end of this book, you'll be able to combine the different solutions you've learned as building blocks to solve real-world ML problems.

Machine Learning with BigQuery ML. Create, execute, and improve machine learning models in BigQuery using standard SQL queries

Alessandro Marrandino

BigQuery ML enables you to easily build machine learning (ML) models with SQL without much coding. This book will help you to accelerate the development and deployment of ML models with BigQuery ML.The book starts with a quick overview of Google Cloud and BigQuery architecture. You'll then learn how to configure a Google Cloud project, understand the architectural components and capabilities of BigQuery, and find out how to build ML models with BigQuery ML. The book teaches you how to use ML using SQL on BigQuery. You'll analyze the key phases of a ML model's lifecycle and get to grips with the SQL statements used to train, evaluate, test, and use a model. As you advance, you'll build a series of use cases by applying different ML techniques such as linear regression, binary and multiclass logistic regression, k-means, ARIMA time series, deep neural networks, and XGBoost using practical use cases. Moving on, you'll cover matrix factorization and deep neural networks using BigQuery ML's capabilities. Finally, you'll explore the integration of BigQuery ML with other Google Cloud Platform components such as AI Platform Notebooks and TensorFlow along with discovering best practices and tips and tricks for hyperparameter tuning and performance enhancement.By the end of this BigQuery book, you'll be able to build and evaluate your own ML models with BigQuery ML.

Machine Learning with Go Quick Start Guide. Hands-on techniques for building supervised and unsupervised machine learning workflows

Michael Bironneau, Toby Coleman

Machine learning is an essential part of today's data-driven world and is extensively used across industries, including financial forecasting, robotics, and web technology. This book will teach you how to efficiently develop machine learning applications in Go.The book starts with an introduction to machine learning and its development process, explaining the types of problems that it aims to solve and the solutions it offers. It then covers setting up a frictionless Go development environment, including running Go interactively with Jupyter notebooks. Finally, common data processing techniques are introduced.The book then teaches the reader about supervised and unsupervised learning techniques through worked examples that include the implementation of evaluation metrics. These worked examples make use of the prominent open-source libraries GoML and Gonum.The book also teaches readers how to load a pre-trained model and use it to make predictions. It then moves on to the operational side of running machine learning applications: deployment, Continuous Integration, and helpful advice for effective logging and monitoring.At the end of the book, readers will learn how to set up a machine learning project for success, formulating realistic success criteria and accurately translating business requirements into technical ones.

Machine Learning with R. Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data - Fourth Edition

Brett Lantz

Dive into R with this data science guide on machine learning (ML). Machine Learning with R, Fourth Edition, takes you through classification methods like nearest neighbor and Naive Bayes and regression modeling, from simple linear to logistic.Dive into practical deep learning with neural networks and support vector machines and unearth valuable insights from complex data sets with market basket analysis. Learn how to unlock hidden patterns within your data using k-means clustering.With three new chapters on data, you’ll hone your skills in advanced data preparation, mastering feature engineering, and tackling challenging data scenarios. This book helps you conquer high-dimensionality, sparsity, and imbalanced data with confidence. Navigate the complexities of big data with ease, harnessing the power of parallel computing and leveraging GPU resources for faster insights.Elevate your understanding of model performance evaluation, moving beyond accuracy metrics. With a new chapter on building better learners, you’ll pick up techniques that top teams use to improve model performance with ensemble methods and innovative model stacking and blending techniques.Machine Learning with R, Fourth Edition, equips you with the tools and knowledge to tackle even the most formidable data challenges. Unlock the full potential of machine learning and become a true master of the craft.