Introduction to AI and Machine Learning on Google Cloud
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.
- Duration: 1 day
- Languages: English, German
- Level: Introductory
- Group size: Up to 16
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.
Prerequisites
- Basic knowledge of machine learning concepts
- Prior experience with programming languages such as SQL and Python
Course outline
Course Introduction
- Course introduction
AI Foundations
- A use case
- AI on Google Cloud
- AI infrastructure
- AI models
- BigQuery ML
- Hands-on lab: Predict Visitor Purchases with BigQuery ML
Generative AI
- 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
AI Development Options
- AI development options
- Vertex AI
- AutoML
- Pre-trained APIs
- Custom training
- Hands-on lab: Entity and Sentiment Analysis with Natural Language API
AI Development Workflow
- 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
Course Summary
- Course summary
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Not sure which course fits?
Book a 30-minute call. We'll look at where your team is, what they need to be able to do, and put together either a specific course date or a tailored learning plan.