Introduction to AI and Machine Learning on Google Cloud
- Código del Curso GO9095
- Duración 1 Día
Otros Métodos de Impartición
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Este curso está disponible en los siguientes formatos:
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Cerrado
Cerrado
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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.
Products:
- Gemini multimodal
- Gemini Enterprise
- NotebookLM
- BigQuery ML
- Vertex AI, Vertex AI Studio, Vertex AI Agent Builder, Vertex AI Pipelines
- Natural Language API
- AutoML
Updated July 2026
Calendario
Parte superiorDirigido a
Parte superiorObjetivos del Curso
Parte superiorAfter this course participants should 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 superiorModule 0: Course Introduction
- Define the course objectives.
- Recognize the course structure
Module 1: AI Foundations
- Recognize the AI/ML framework on Google Cloud.
- Identify the major components of AI infrastructure.
- Define the data and ML products on Google Cloud and how they support the data-to-AI lifecycle.
- Build an ML model with BigQuery ML to bring data to AI.
- Lab: Predict Visitor Purchases with BigQuery ML
Module 2: Generative AI
- Define generative AI and foundation models.
- Recognize the prompt-to-production lifecycle and its associated tools.
- Define AI agents and their core components.
- Identify Google Cloud tools and technologies for building AI agents.
- Lab: Get started with Vertex AI Studio
Module 3: AI Development Options
- Define different options to build an ML model with Vertex AI on Google Cloud.
- Identify the features and use cases of pre-trained APIs, AutoML, and custom training.
- Use the Natural Language API to analyze text.
- Lab: Entity and Sentiment Analysis with Natural Language API
Module 4: 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 with Vertex AI.
- Lab: Vertex AI: Predict Loan Risk with AutoML
Module 5: Course Summary
- Recognize the primary concepts, tools, technologies, and products learned in the course.
Pre-requisitos
Parte superiorHaving one or more of the following:
- Basic knowledge of machine learning concepts
- Prior experience with programming languages such as SQL and Python
Certificación de Prueba
Parte superiorNone
Siguientes Cursos Recomendados
Parte superiorNo recommendation
Más información
Parte superior- Official course book provided to participants
