SC-T-AIMLGC-BOfficial Google Curriculum
Introduction to AI and Machine Learning on Google Cloud (Scaled)
1 dayILTIntroductoryLoading...
Overview
This course introduces Google Cloud's AI and machine learning (ML) capabilities, with a focus on developing both generative and predictive AI projects. It explores the various technologies, products, and tools available throughout the data-to-AI lifecycle, empowering data scientists, AI developers, and ML engineers to enhance their expertise through interactive exercises.
What You'll Learn
- Recognize the data-to-AI technologies and tools offered by Google Cloud.
- Build generative AI projects by using Gemini multimodal, efficient prompts, and AI agent builders.
- Choose between different Google Cloud product options to develop an AI project.
- Build ML models end to end by using Vertex AI.
Who Should Attend
AI developers, data scientists, and ML engineers
Prerequisites
Basic knowledge of machine learning concepts. Prior experience with programming languages such as SQL and Python
Products Covered
Gemini multimodalVertex AIVertex AI StudioVertex AI Agent BuilderVertex AI PipelinesGemini EnterpriseNotebookLMBigQuery MLNatural Language APIAutoML
Course Modules
1
Course Introduction
Topics
- Course introduction
Learning Outcomes
- Define the course objectives.
- Recognize the course structure.
2
AI Foundations
Topics
- A use case
- AI on Google Cloud
- AI infrastructure
- AI models
- BigQuery ML
- Hands-on lab: Predict Visitor Purchases with BigQuery ML
Learning Outcomes
- 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.
Activities
Lab: Predict Visitor Purchases with BigQuery MLQuizReading
3
Generative AI
Topics
- Generative AI on Google Cloud
- Foundation models
- Idea to app
- Prompt engineering
- Deployment and model tuning
- AI agents
- Agent building with Google Cloud
- Hands-on lab: Get started with Vertex AI Studio
Learning Outcomes
- 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.
Activities
Lab: Get started with Vertex AI StudioQuizReading
4
AI Development Options
Topics
- AI development options
- Vertex AI
- AutoML
- Pre-trained APIs
- Custom training
- Hands-on lab: Entity and Sentiment Analysis with Natural Language API
Learning Outcomes
- 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.
Activities
Lab: Entity and Sentiment Analysis with Natural Language APIQuizReading
5
AI Development Workflow
Topics
- ML workflow
- Data preparation
- Model development
- Model serving
- MLOps and workflow automation
- How a machine learns (optional)
- Hands-on lab: Vertex AI: Predict Loan Risk with AutoML
Learning Outcomes
- 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.
Activities
Lab: Vertex AI: Predict Loan Risk with AutoMLQuizReading
6
Course Summary
Topics
- Course summary
Learning Outcomes
- Recognize the primary concepts, tools, technologies, and products learned in the course.
Activities
Reading
Get This Training
No public classes currently scheduled. Express interest below or request private training.
Course Details
- Course Code
- SC-T-AIMLGC-B
- Duration
- 1 day
- Format
- ILT
- Level
- Introductory
- Modules
- 6
- Activities
- 13
- Price
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