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

Medical professionals are often involved in the management of the pregnant patient without necessarily being experts on all the complications surrounding pregnancy. The Handbook of Obstetric Medicine addresses the most common and serious medical conditions encountered in pregnancy, including heart disease, thromboembolism, diabetes, skin problems,

Recognition of the importance of maternal medicine is now reflected in the content of the MRCOG exam, core training and higher training in both obstetrics and medicine. This book approaches obstetric medicine from the point of view of real patients and clinical scenarios as well as model answers to exam questions. The book will be invaluable for trainees and consultants who want to ‘test themselves’.

Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering,and clearly explains how to apply the best practices from software engineering to data science. Examples are provided in Python, drawn from popular packages such as NumPy and pandas. If you want to write better data science code, this guide covers the essential topics that are often missing from introductory data science or coding classes, including how to: Understand data structures and object-oriented programming Clearly and skillfully document your code Package and share your code Integrate data science code with a larger code base Learn how to write APIs Create secure code Apply best practices to common tasks such as testing, error handling, and logging Work more effectively with software engineers Write more efficient, maintainable, and robust code in Python Put your data science projects into production And more

Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering,and clearly explains how to apply the best practices from software engineering to data science. Examples are provided in Python, drawn from popular packages such as NumPy and pandas. If you want to write better data science code, this guide covers the essential topics that are often missing from introductory data science or coding classes, including how to: Understand data structures and object-oriented programming Clearly and skillfully document your code Package and share your code Integrate data science code with a larger code base Learn how to write APIs Create secure code Apply best practices to common tasks such as testing, error handling, and logging Work more effectively with software engineers Write more efficient, maintainable, and robust code in Python Put your data science projects into production And more

The field of obstetric medicine is vast since virtually any medical condition may coincide with pregnancy, but certain diseases are encountered more frequently in women of child-bearing age and therefore more frequently complicate pregnancy. As the body of scientific knowledge grows and we understand more about conditions such as pre-eclampsia and intrahepatic cholestatis of pregnancy, the specialty of obstetric medicine continues to develop. This handbook provides a practical guide for obstetricians and physicians of all grades who care for pregnant women with medical problems. Highlighting the most important and relevant factors in the management of medical complications in pregnancy, the authors provide a synthesis of contemporary literature review and established practice. The chapters in the main section of the book are organized by system and cover the most common and important medical conditions encountered in pregnancy. This is followed by a section providing a practical approach to common symptoms in pregnancy with an emphasis on differential diagnosis.

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. The book also explores new approaches for integrating data privacy into machine learning pipelines. Understand the machine learning management lifecycle Implement data pipelines with Apache Airflow and Kubeflow Pipelines Work with data using TensorFlow tools like ML Metadata, TensorFlow Data Validation, and TensorFlow Transform Analyze models with TensorFlow Model Analysis and ship them with the TFX Model Pusher Component after the ModelValidator TFX Component confirmed that the analysis results are an improvement Deploy models in a variety of environments with TensorFlow Serving, TensorFlow Lite, and TensorFlow.js Learn methods for adding privacy, including differential privacy with TensorFlow Privacy and federated learning with TensorFlow Federated Design model feedback loops to increase your data sets and learn when to update your machine learning models

The 54th RCOG Study Group brought together a range of experts from across many disciplines to examine the most up-to-date evidence on all aspects of diagnosis and management in women with renal problems before, during and after pregnancy. This book presents the findings of the Study Group, describing many of the issues likely to be faced in clinical practice and providing valuable information for all healthcare professionals working in this field. General principles for optimal management are clearly defined and separate chapters are devoted to specific disease entities and/or clinical situations. Many controversial areas - such as management of hypertension, diagnosis of pre-eclampsia, assisted conception, rationalisation of the many medications used in nephrology practice, renal biopsy, surgical emergencies, patient input and responsibilities - are carefully considered.