Using machine learning and artificial intelligence (AI) in existing business processes has been successful. Even AWS's ML and AI services make it simple and economical to conduct machine learning experiments. This book will show readers how to use the complete set of AI and ML services available on AWS to streamline the management of their whole AI operation and speed up their innovation.In this book, you'll learn how to build data lakes, build and train machine learning models, automate M...

Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models.Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation...

New edition of the bestselling guide to artificial intelligence with Python, updated to Python 3.x, with seven new chapters that cover RNNs, AI and Big Data, fundamental use cases, chatbots, and more.Key FeaturesCompletely updated and revised to Python 3.xNew chapters for AI on the cloud, recurrent neural networks, deep learning models, and feature selection and engineeringLearn more about deep learning algorithms, machine learning data pipelin...

Each chapter of the book quickly introduces a key theme of Data Analysis, before immersing you in the practical aspects of each theme. Youll learn quickly how to perform all aspects of Data Analysis.Practical Data Analysis is a book ideal for home and small business users who want to slice & dice the data they have on hand with minimum hassle.

Learn 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 with cloud native tools on GCP.Throughout this updated second edition, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by building a data pipeline in your own ...

Leverage the power of Tensorflow to design deep learning systems for a variety of real-world scenariosKey Features\[\*\]Build efficient deep learning pipelines using the popular Tensorflow framework\[\*\]Train neural networks such as ConvNets, generative models, and LSTMs\[\*\]Includes projects related to Computer Vision, stock prediction, chatbots and moreBook DescriptionTensorFlow is one of the most popular frameworks used ...

If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable.Using examples th...

Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book provides a thorough overview of the Microsoft Azure Machine Learning service released for general availability on February 18th, 2015 with practical guidance for building recommenders, propensity models, and churn and predictive maintenance models.

Object-Oriented Analysis and Design for Information Systems clearly explains real object-oriented programming in practice. Expert author Raul Sidnei Wazlawick explains concepts such as object responsibility, visibility and the real need for delegation in detail. The object-oriented code generated by using these concepts in a systematic way is concise, organized and reusable. The patterns and solutions presented in this book are based in research and industrial applications. You will come a...

Get command of your organizational Big Data using the power of data science and analytics Key Features\[\*\] A perfect companion to boost your Big Data storing, processing, analyzing skills to help you take informed business decisions\[\*\] Work with the best tools such as Apache Hadoop, R, Python, and Spark for NoSQL platforms to perform massive online analyses\[\*\] Get expert tips on statistical inference, machine learning, mathematical mode...

Get up and running with machine learning life cycle management and implement MLOps in your organizationKey FeaturesBecome well-versed with MLOps techniques to monitor the quality of machine learning models in productionExplore a monitoring framework for ML models in production and learn about end-to-end traceability for deployed modelsPerform CI/CD to automate new implementations in ML pipelinesBook DescriptionEngineering MLp...

Become a master at penetration testing using machine learning with PythonKey FeaturesIdentify ambiguities and breach intelligent security systemsPerform unique cyber attacks to breach robust systemsLearn to leverage machine learning algorithmsBook DescriptionCyber security is crucial for both businesses and individuals. As systems are getting smarter, we now see machine learning interrupting computer security. With the adopti...