Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions Key Features Explore different ML tools and frameworks to solve large-scale machine learning challenges in the cloud Build an efficient data science environment for data exploration, model building, and model training Learn how to implement bias detection, privacy, and explainability in ML model development Book Description...

Build your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts. This book shows you how to build a deep learning pipeline for real-life TensorFlow projects. You'll learn what a pipeline is and how it works so you can build a full application easily and rapidly. Then troubleshoot and overcome basic Tensorflow obstacles to easily create functional apps and deploy well-trained models. Step-by-step and example-oriented...

Deep learning has the reputation as an exclusive domain for math PhDs. Not so. With this book, programmers comfortable with Python will learn how to get started with deep learning right away. Using PyTorch and the fastai deep learning library, you'll learn how to train a model to accomplish a wide range of tasks-including computer vision, natural language processing, tabular data, and generative networks. At the same time, you'll dig progressively...

Machine learning has accelerated in several industries recently, enabling companies to automate decisions and act based on predicted futures. In time, nearly all major industries will embed ML into the core of their businesses, but right now the gap between companies that successfully adopt ML and those that fail continues to grow. This report examines why so many ML initiatives stall, especially at the stage of moving models from proof of concept...

Master machine learning concepts and develop real-world solutions Machine learning offers immense opportunities, and Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the foundations of artificial intelligence and the basic steps of any machine learning project. Next, they introduce Microsoft's powerful ML.NET library, including capabilities for data processing,...

Until recently, an organization would have had to collect and store data in a central location to train a model with machine learning. Now, federated learning offers an alternative. With this report, you'll learn how to train ML models without sharing sensitive data in the process. Google software engineers Emily Glanz and Nova Fallen introduce the motivation and technologies behind federated learning, providing the context you need to integrate it...

Learn how to leverage feature stores to make the most of your machine learning models Key Features Understand the significance of feature stores in the ML life cycle Discover how features can be shared, discovered, and re-used Learn to make features available for online models during inference Book Description Feature store is one of the storage layers in machine learning (ML) operations, where data scientists and ML engineers can store transformed...

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Machine learning is the key to digital transformation for many businesses today. This process accelerates your company's ability to engage customers, optimize operations, empower employees, and fundamentally change and transform products. One of the best ways to get started is with Automated Machine Learning, a quick and efficient method to jumpstart your organization's use of machine learning. In this report, AI leader Wee Hyong Tok explains how...