Introduction to AI and Machine Learning on Google Cloud
- Código del Curso GO9091
- Duración 1 Día
Otros Métodos de Impartición
Método de Impartición
Este curso está disponible en los siguientes formatos:
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Clase de calendario
Aprendizaje tradicional en el aula
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Aprendizaje Virtual
Aprendizaje virtual
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Temario
Parte superiorThis 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.
Calendario
Parte superiorDirigido a
Parte superiorThis course is intended for Data scientists, AI developers and ML engineers.
Objetivos del Curso
Parte superiorOn completion of this course, students will be able to
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.
Contenido
Parte superiorIntroduction
This module covers the course objective of helping learners navigate the AI development tools on Google Cloud. It also provides an overview of the course structure, which is based on a three-layer AI framework including AI foundations, development, and solutions.
AI Foundations
This module begins with a use case demonstrating the AI capabilities. It then focuses on the AI foundations including cloud infrastructure like compute and storage. It also explains the primary data and AI development products on Google Cloud. Finally, it demonstrates how to use BigQuery ML to build an ML model, which helps transition from data to AI.
AI Development Options
This module explores the various options for developing an ML project on Google Cloud, from ready-made solutions like pre-trained APIs, to no-code and low-code solutions like AutoML, and code-based solutions like custom training. It compares the advantages and disadvantages of each option to help decide the right development tools.
AI Development Workflow
This module walks through the ML workflow from data preparation, to model development, and to model serving on Vertex AI. It also illustrates how to convert the workflow into an automated pipeline using Vertex AI Pipelines.
Generative AI
This module introduces generative AI (gen AI), the newest advancement in AI, and the essential toolkits for developing gen AI projects. It starts by examining the gen AI workflow on Google Cloud. It then investigates how to use Gen AI Studio and Model Garden to access Gemini multimodal, design prompt, and tune models. Finally, it explores the built-in gen AI capabilities of AI solutions.
Summary
This module provides a summary of the entire course by covering the most important concepts, tools, technologies, and products.
Pre-requisitos
Parte superiorStudents are advised to have the following;
Basic knowledge of machine learning concepts
Prior experience with programming languages such as SQL and Python
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