Machine Learning
Vertex AI Model Garden
Revolutionize your machine learning capabilities with Vertex AI Model Garden. Learn how to effectively use foundation models, task-specific models, and APIs to scale your AI solutions and drive business innovation
1 Days
Target Audience
Who should attend?
Machine learning practitioners eager to utilize models in Vertex AI Model Garden for diverse applications and innovative use cases.
What you'll learn
Explore Vertex AI Model Garden for diverse model options.Incorporate models into ML workflows for enhanced solutions.Leverage and fine-tune foundation models for tailored generative AI use cases.
Prerequisites for Success
Prerequisites for Success
• Completed the Machine Learning on Google Cloud course or possess equivalent knowledge of TensorFlow, Keras, and machine learning concepts.
• Some experience with Python scripting and working in Jupyter notebooks to create machine learning models.
COURSE AGENDA
Vertex AI for ML Workloads
- Vertex AI on Google Cloud: Understand how Vertex AI integrates into Google Cloud for machine learning workloads.
- Training, Tuning, and Deploying ML Models: Explore various options available in Vertex AI for training, tuning, and deploying machine learning models.
- Generative AI Options: Discover the Generative AI options available within Google Cloud and Vertex AI for advanced applications.
Model Garden
- Introduction to Model Garden: Learn about the Model Garden platform and how it enables easy access to pre-built models.
- Model Types in Model Garden: Explore different model types available within the Model Garden.
- Connecting Models: Understand how to connect models from Gen AI Studio and the Model Registry for enhanced functionality.
- Course Use Cases: Introduction to the use cases that will be explored throughout the course.
Task-Specific Solutions: Content Classification
- Pre-trained Models for Specific Tasks: Learn how to leverage pre-trained models for specific machine learning tasks such as content classification.
- Vertex AI AutoM: Understand how to use Vertex AI AutoML for automated model training.
- Using Pre-trained Models via Python SDK: Learn to interact with pre-trained models using the Python SDK.
- Lab: Implement Content Classification using Natural Language API and AutoML.
Foundation Models: Text Embeddings via PaLM
- Introduction to Foundation Models: Understand the role of foundation models in machine learning workflows.
- PaLM API: Get familiar with the PaLM API for working with text embeddings.
- GenAI Studio: Explore how GenAI Studio integrates with foundation models for enhanced data processing.
- Using the Embeddings API: Learn how to use the Embeddings API for semantic analysis.
- Lab: Use the PaLM API to cluster products based on their descriptions.
Fine-Tunable Models
- Fine-Tunable Models in Model Garden: Discover fine-tunable models in Model Garden and their advantages for custom applications.
- Vertex AI Pipelines: Learn how to automate and scale model tuning using Vertex AI Pipelines.
- Demo: Watch a demonstration on fine-tuning models for your specific use case.