Part of Microsoft's radical WinFX API is the Indigo foundation, more formally known as the Windows Communication Foundation, or WCF. "Pro WCF: Practical Microsoft SOA Implementation" is a complete guide to WCF from the service-oriented architecture (SOA) perspective and shows you why WCF is important to web service development and architecture.

The book covers the unified programming model, reliable messaging, security, the peer-to-peer programming model, and more. Youll also learn how to move your current DCOM and .NET remoting applications to WCF, and how to integrate those applications with new WCF-based applications. Youll want to get ahold of a copy because it: Contains a comprehensive WCF programming model Explains how queue management and reliable messaging work in WCF Discusses implementing transaction support in WCF Shows how to make WCF services interoperable with other SOA offerings Thoroughly covers WCF security topics and concerns

Most data scientists and engineers today rely on quality labeled data to train machine learning models. But building a training set manually is time-consuming and expensive, leaving many companies with unfinished ML projects. There's a more practical approach. In this book, Wee Hyong Tok, Amit Bahree, and Senja Filipi show you how to create products using weakly supervised learning models. You'll learn how to build natural language processing and computer vision projects using weakly labeled datasets from Snorkel, a spin-off from the Stanford AI Lab. Because so many companies have pursued ML projects that never go beyond their labs, this book also provides a guide on how to ship the deep learning models you build. Get up to speed on the field of weak supervision, including ways to use it as part of the data science process Use Snorkel AI for weak supervision and data programming Get code examples for using Snorkel to label text and image datasets Use a weakly labeled dataset for text and image classification Learn practical considerations for using Snorkel with large datasets and using Spark clusters to scale labeling