Build AI quickly, collaboratively, and with confidence with Colab using Vertex AI
2 min read
5 months ago
Published on Jul 12, 2024
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Table of Contents
Tutorial: Building AI with Colab on Vertex AI
Step 1: Introduction
- The video introduces Nenshad Bardoliwalla and Karthik Ramachandran, who discuss building AI quickly, collaboratively, and with confidence using Colab on Vertex AI.
Step 2: Understanding Colab Enterprise
- Colab Enterprise offers enterprise-level security, compliance support, repeatability, and more for scaling AI projects.
- It was launched in November 2017 and has since added various features like AlphaFold and Auto Charting.
Step 3: Leveraging Google Research Capabilities
- Google Cloud provides access to cutting-edge research from Google DeepMind and research teams.
- Products like TensorFlow and neural architecture search have been democratized for broader use.
Step 4: Using Colab Enterprise
- Colab Enterprise offers managed notebook storage and an editor for a seamless experience.
- It provides zero-config compute options and flexible compute options for different needs.
Step 5: Collaboration and Versioning
- Colab Enterprise allows for collaboration through IAM permissions and offers automatic versioning to prevent data loss.
Step 6: Accessing Powerful Compute
- Administrators can create runtime templates for users to access powerful compute options.
- Users can connect to existing runtimes or create new ones based on templates.
Step 7: Fine-Tuning Models with Colab Enterprise
- Users can fine-tune foundation models using the SDK within Colab Enterprise.
- The process involves pulling models, initiating tuning jobs, and evaluating model performance.
Step 8: Conclusion and Access
- Colab Enterprise is available in public preview and can be accessed via BigQuery Studio through an allow list.
- Users can leverage the code generation feature for enhanced productivity.
By following these steps, you can effectively utilize Colab on Vertex AI for building AI projects quickly and confidently.