Google Machine Learning and Generative AI for Solutions Architects: Build efficient and scalable AI/ML solutions on Google Cloud

Learn how to architect and run real-world AI/ML solutions at scale on Google Cloud, as well as best practices, common industry challenges, and how to address those challenges effectively. Key Features Understand the AI/ML landscape on Google Cloud Learn Data preparation and model development Implement MLOps and scaling production workloads with Google Cloud Book Description
Almost every company nowadays is either using or trying to use AI/ML in some way. While AI/ML research is undoubtedly complex, what is often more complex is actually building and running applications that use AI/ML effectively. This book teaches you how to successfully design and run AI/ML workloads, based on years of experience implementing large-scale and highly complex AI/ML projects at some of the world’s leading technology companies.
Understand the common challenges that companies run into when implementing AI/ML workloads, and industry-proven best practices to overcome those challenges. Learn about the vast AI/ML landscape on Google Cloud and how to implement all of the required steps in a typical AI/ML project. Use services such as BigQuery to prepare data, and Vertex AI to train, deploy, monitor, and scale models in production, as well as MLOps to automate the entire process.
Suitable both for beginners and experienced practitioners, it begins by covering important fundamental AI/ML concepts, and then builds in complexity through examples and hands-on activities to eventually dive deep into advanced, cutting-edge AI/ML applications that address real-world use-cases in today’s market. What you will learn ?Learn about the various AI/ML offerings on Google Cloud, and how they can be used to address specific business problems ?Learn how to source, understand, and prepare data for ML workloads ?Build, train, and deploy ML models on Google Cloud ?Learn how to build an effective MLOps strategy and implement MLOps workloads on Google Cloud Who this book is for
People aspiring to become Solution Architects, who want to know how to design and implement AI/ML solutions on Google Cloud.
Basic knowledge of Python and ML concept required. This book will briefly cover the basics at the beginning in order to establish a baseline for the readers, but it will not go into depth on the underlying mathematical concepts that the readers could learn from academic materials. It will focus on how to use AI/ML in the real world on Google Cloud Table of Contents AI/ML concepts and real-world applications Understanding the ML model development lifecycle AI/ML tooling and the Google Cloud AI/ML landscape Utilizing Google Cloud's high-level AI services Building custom ML models on Google Cloud Diving deeper: preparing and processing data for AI/ML workloads on Google Cloud Feature engineering and dimensionality reduction Hyperparameters and optimization Governance, Explainability, Fairness (Bias), and Privacy Deploying, monitoring, and scaling in productionDeploying, monitoring, and scaling in production Machine Learning Engineering and MLOps with GCP AI/ML workload performance and cost optimization ML Governance and the Google Cloud Architecture Framework Advanced use-cases and technologies An Introduction to Generative AI Generative AI on Google Cloud Advanced Generative AI concepts and use cases Bringing it all together: Building ML Solutions with GCP and Vertex