Recruitment of participants Google Machine Learning and Generative AI for Solutions Architects: Build efficient and scalable AI

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



Key benefits

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  • Understand key concepts, from fundamentals through to complex topics, via a methodical approach
  • Build real-world end-to-end MLOps solutions and generative AI applications on Google Cloud
  • Get your hands on a code repository with over 20 hands-on projects for all stages of the ML model development lifecycle
  • Purchase of the print or Kindle book includes a free PDF eBook

Description
Nearly 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 learn

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  • Build solutions with open-source offerings on Google Cloud, such as TensorFlow, PyTorch, and Spark
  • Source, understand, and prepare data for ML workloads
  • Build, train, and deploy ML models on Google Cloud
  • Create an effective MLOps strategy and implement MLOps workloads on Google Cloud
  • Discover common challenges in typical AI/ML projects and get solutions from experts
  • Explore vector databases and their importance in Generative AI applications
  • Uncover new Gen AI patterns such as Retrieval Augmented Generation (RAG), agents, and agentic workflows