Pragmatic AI: An Introduction to Cloud-Based Machine Learning
Pragmatic AI: An Introduction to Cloud-Based Machine Learning offers a hands-on approach to understanding how artificial intelligence and machine learning are applied in real-world cloud environments. The book provides practical guidance for developers, data scientists, and engineers who want to integrate AI into scalable production systems using modern cloud platforms like AWS, Azure, and Google Cloud. It covers essential topics such as supervised and unsupervised learning, model deployment, MLOps, data preprocessing, and the use of open-source tools like TensorFlow, PyTorch, and Scikit-learn. The book emphasizes the "pragmatic" use of AI, focusing on building solutions that are maintainable, reproducible, and deployable in cloud environments. Readers learn not just the theoretical foundations of AI, but also how to architect and execute machine learning pipelines in enterprise settings. Gift's step-by-step tutorials and examples make it easier to grasp concepts like model training, evaluation, and continuous integration for AI systems. A strong emphasis is placed on automation, DevOps practices, and monitoring AI models post-deployment. Ultimately, Pragmatic AI serves as a bridge between data science theory and cloud-based software engineering, helping professionals understand how to transform machine learning prototypes into robust, real-world applications. Pragmatic AI: An Introduction to Cloud-Based Machine Learning is a practical guide that bridges the gap between AI theory and real-world cloud implementation. Contents: 1. Cloud Computing, 2. Development and Evolution of Artificial Intelligence, 3. Cloud Computing Environment, 4. Automated Machine Learning, 5. Understand the Risks of Cloud Computing, 6. Cloud Service Architecture, 7. Analytics Analysis in Machine Learning.