This course introduces the AI and machine learning (ML) offerings on Google Cloud that build both predictive and generative AI projects. It explores the technologies, products, and tools available throughout the data-to-AI life cycle, encompassing AI foundations, development, and solutions. It aims to help data scientists, AI developers, and ML engineers enhance their skills and knowledge through engaging learning experiences and practical hands-on exercises. Introduction to AI and Machine Learning on Google Cloud Benefits In this course, you will: Recognize the data-to-AI technologies and tools provided by Google Cloud. Build generative AI projects by using Gemini multimodal, efficient prompts, and model tuning. Explore various options for developing an AI project on Google Cloud. Create an ML model from end-to-end by using Vertex AI. Prerequisites: Basic knowledge of machine learning concepts Prior experience with programming languages such as SQL and Python Introduction to AI & ML on Google Cloud Training Outline Learning Objectives Module 1: AI Foundations Recognize the AI/ML framework on Google Cloud. Identify the major components of Google Cloud infrastructure Define the data and ML products on Google Cloud and how they support the data-to-AI lifecycle. Build an ML model with BigQueryML to bring data to AI Module 2: AI Development Options Define different options to build an ML model on Google Cloud. Recognize the primary features and applicable situations of pre-trained APIs, AutoML, and custom training. Use the Natural Language API to analyze text. Module 3: AI Development Workflow Define the workflow of building an ML model. Describe MLOps and workflow automation on Google Cloud. Build an ML model from end to end by using AutoML on Vertex AI. Module 4: Generative AI Define generative AI and foundation models. Use Gemini multimodal with Vertex AI Studio. Design efficient prompt and tune models with different methods. Recognize the AI solutions and the embedded Gen AI features.
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