Overview of Google Cloud for Machine Learning and AI

Figure 1

Architecture diagram showing a Workbench notebook at the center orchestrating four managed services via SDK calls: Training Jobs (Eps 4-5), HP Tuning Jobs (Ep 6), Cloud Storage (Ep 3), and Gemini API (Ep 7).
Notebook as controller — overview of workshop architecture

Notebooks as Controllers

Figure 1

  • Network / Subnetwork: Leave as pre-filled. Notebook settings (part1)

    • Figure 2

    Screenshot showing required tags for notebook
    Required tags for notebook.

    Figure 3

    Set Idle Shutdown
    Enable Idle Shutdown

    Figure 4

    Architecture diagram showing how a lightweight Workbench notebook uses the Vertex AI SDK to launch training jobs and HP tuning jobs on powerful GPUs, with all artifacts stored in GCS.
    Training and tuning workflow

    Data Storage and Access

    Training Models in Vertex AI: Intro

    Figure 1

    Architecture diagram showing how a lightweight Workbench notebook uses the Vertex AI SDK to launch training jobs and HP tuning jobs on powerful GPUs, with all artifacts stored in GCS.
    Training and tuning workflow

    Training Models in Vertex AI: PyTorch Example

    Hyperparameter Tuning in Vertex AI: Neural Network Example

    Retrieval-Augmented Generation (RAG) with Vertex AI

    Figure 1

    Architecture diagram showing the RAG pipeline: a Workbench notebook orchestrates document chunking, embedding via the Gemini API, and retrieval-augmented generation, with documents and embeddings stored in a GCS bucket.
    RAG pipeline with Gemini API

    Bonus: CLI Workflows Without Notebooks

    Resource Management & Monitoring on Vertex AI (GCP)