Course: Generative AI on Google Cloud Platform (GCP)

Generative artificial intelligence (AI) has taken the world by storm. Chatbots, photo creation, document writing, and other practical applications are everywhere, and they're gaining in popularity and sophistication. Google Cloud Platform (GCP) has a broad range of powerful generative AI tools that can be used to leverage the power of modern artificial intelligence. In this course, you'll be introduced to generative AI, beginning with GCP and its generative AI offerings. You will discover the advantages and disadvantages of generative AI and features of GCP machine learning (ML). Then you'll learn about the generative AI life cycle, image generation, natural language processing (NLP), and best practices for developing generative AI. Finally, you'll explore GCP privacy, security, and compliance considerations, monitoring and logging with GCP, and some real-world generative AI use cases.

Google Cloud Platform (GCP) has a broad range of powerful generative AI tools that can be used to leverage the power of modern artificial intelligence (AI). A main component of that toolbox is Google's Vertex AI platform, which can aid organizations in streamlining machine learning (ML) workflows and deliver better machine learning models. In this course, you'll be introduced to Vertex AI, beginning with an overview of its features, model deployment, and end-to-end workflow. Then you'll explore pros and cons of Vertex AI, how it can be used to accelerate development, and model selection, training, and evaluation considerations. Finally, you'll learn about Vertex AI integration, data preparation, feature engineering, model evaluation, and Vertex AI success stories.

A main component of the Google Cloud Platform (GCP) machine learning offering is Google's Vertex AI Platform, which provides tools that can aid organizations in leveraging Vertex AI for generative artificial intelligence (GenAI) projects. This course investigates the unique support and capabilities offered by Vertex AI specifically for generative AI models, empowering learners to unlock their creative potential. In this course, you will dig into harnessing generative AI using Vertex AI, beginning with an overview of generative AI support in Vertex AI, Vertex AI's generative AI models, and the features and uses of Google's Generative AI Studio. Then you will discover the Vertex AI API, explore Vertex AI Model Garden, and create custom models in Vertex AI. Finally, you will learn about responsible AI with safety filters and find out how to use Vertex AI Search and Conversation. After completing this course, you will be able to confidently leverage Vertex AI to build generative AI models.

Generative artificial intelligence (AI) has taken the world by storm, and Google Cloud Platform (GCP) has a broad range of powerful generative AI tools that can be used to leverage the power of modern artificial intelligence. Google's Generative AI Studio is a cloud console tool for rapid prototyping and testing of generative AI models and leverages the power of Vertex AI, allowing developers to interact, tune, and deploy large-scale AI models. In this course, you will take a deep dive into Generative AI Studio, beginning with generative AI prompt design, model training, and tuning language models. Then you will explore model performance evaluation and generative AI deployment. Next, you will focus on case studies and practical examples to discover how organizations use GCP generative AI. Finally, you will learn how to convert speech to text and prototype language applications.

Google Cloud Platform (GCP) has a wide range of powerful generative artificial intelligence (AI) services that can leverage the power of modern AI. Using these services, developers can build powerful applications with GCP, and with the proper use of various technologies, developers new to GCP can gain the knowledge and skills necessary to develop and deploy cutting-edge generative AI applications. In this course, you will create a generative AI-powered app in GCP, beginning with planning and building a generative AI model, GCP environment setup, and Python environment creation. Then you will learn how to build and train a generator and a discriminator with TensorFlow. Next, you will build and run a training loop, and dockerize the training script. Finally, you will deploy your finished model to an application programming interface (API) endpoint and test that model.