Architect and run real-world AI/ML solutions at scale on Google Cloud, and discover best practices to address common industry challenges effectivelyKey FeaturesUnderstand key concepts, from fundamentals through to complex topics, via a methodical approachBuild real-world end-to-end MLOps solutions and generative AI applications on Google CloudGet your hands on a code repository with over 20 hands-on projects for all stages of the ML model development lifecyclePurchase of the print or Kindle book includes a free PDF eBookBook DescriptionNearly all companies nowadays either already use or are trying to incorporate AI/ML into their businesses. While AI/ML research is undoubtedly complex, the building and running of apps that utilize AI/ML effectively is tougher.

This book shows you exactly how to design and run AI/ML workloads successfully using years of experience some of the world’s leading tech companies have to offer. You’ll begin by gaining a clear understanding of essential fundamental AI/ML concepts, before moving on to grasp complex topics with the help of examples and hands-on activities.

This will help you eventually explore advanced, cutting-edge AI/ML applications that address real-world use cases in today’s market. As you advance, you’ll recognize the common challenges that companies face when implementing AI/ML workloads, and discover industry-proven best practices to overcome these challenges.

The chapters also teach you about the vast AI/ML landscape on Google Cloud and how to implement all the steps needed in a typical AI/ML project. You’ll use services such as BigQuery to prepare data; Vertex AI to train, deploy, monitor, and scale models in production; as well as MLOps to automate the entire process.

By the end of this book, you will be able to unlock the full potential of Google Cloud's AI/ML offerings. What you will learnBuild solutions with open-source offerings on Google Cloud, such as TensorFlow, PyTorch, and SparkSource, understand, and prepare data for ML workloadsBuild, train, and deploy ML models on Google CloudCreate an effective MLOps strategy and implement MLOps workloads on Google CloudDiscover common challenges in typical AI/ML projects and get solutions from expertsExplore vector databases and their importance in Generative AI applicationsUncover new Gen AI patterns such as Retrieval Augmented Generation (RAG), agents, and agentic workflowsWho this book is forThis book is for aspiring solutions architects looking to design and implement AI/ML solutions on Google Cloud.

Although this book is suitable for both beginners and experienced practitioners, basic knowledge of Python and ML concepts is required. The book focuses on how AI/ML is used in the real world on Google Cloud.

It briefly covers the basics at the beginning to establish a baseline for you, but it does not go into depth on the underlying mathematical concepts that are readily available in academic material. Table of ContentsAI/ML Concepts, Real-World Applications, and ChallengesUnderstanding the ML Model Development LifecycleAI/ML Tooling and the Google Cloud AI/ML LandscapeUtilizing Google Cloud's High-Level AI ServicesBuilding Custom ML Models on Google CloudDiving Deeper—Preparing and Processing Data for AI/ML Workloads on Google CloudFeature Engineering and Dimensionality ReductionHyperparameters and OptimizationNeural Networks and Deep LearningDeploying, Monitoring, and Scaling in ProductionMachine Learning Engineering and MLOps with GCP(N.

B. Please use the Read Sample option to see further chapters).