OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Developer Tools & SDKs

Asl Ml Immersion

Asl Ml Immersion is a public, Apache-2.0-licensed repository of Jupyter notebooks, code samples and reference materials for learning and developing machine-learning and generative-AI solutions on Google Cloud. Its modules cover model architectures, cloud MLOps workflows, and generative AI and agent systems.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Asl Ml Immersion — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want hands-on, Google Cloud-oriented learning materials and can set up a supported cloud notebook environment. Start with the topic-specific labs and consult their solution notebooks as needed.

Who it is for

  • Learners working through machine-learning labs and solution notebooks.
  • Instructors or teams creating bootcamp exercises and technical learning materials.
  • Developers exploring Google Cloud model training, tuning, serving and MLOps examples.

Documented uses

  • Study model architectures for tabular, image, text and time-series data.
  • Complete notebook labs and compare work with provided solutions.
  • Explore operational workflows for TensorFlow, Scikit-learn and PyTorch models.
  • Build learning demonstrations around Gemini and Google ADK agent systems.
Before you choose

Requirements & limits

The repository is public and its code is licensed under Apache 2.0. The README says Google Cloud usage may incur charges and that only Googlers can currently contribute.

  • Materials are intended to run on Google Cloud, with setup instructions for Agent Platform Workbench and Cloud Workstations.
  • The modules use different environments and materials; notebooks should be opened with their corresponding Jupyter kernel.
  • GPU or TPU acceleration is not required for most notebooks, although some recommend it.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the notebook environment

Provision a Google Cloud learning environment and build the repository's virtual environments and Jupyter kernels.

Before you start

  • Google Cloud Shell and a Google Cloud project.
  • A supported environment: Agent Platform Workbench or Cloud Workstations.

Steps

  1. Clone the repository in Cloud Shell and run the command below.

    Documented command
    bash scripts/setup_env.sh

  2. Choose Workbench, Workstations, both, or infrastructure-only setup; choose whether to attach an Nvidia T4 GPU.

  3. Open the running environment, clone the repository there, and run make from its directory.

  4. Open a notebook and select its matching module kernel: ASL Core, ASL MLOps or ASL Agent.

Expected outcome

The repository's notebook environment and module kernels are prepared for working through its learning materials.

Guide limitations

  • Some notebooks require additional setup described in their own instructions.
  • Most notebooks do not require accelerators, though some recommend them.
  • Google Cloud product usage may incur charges.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
10 October 2026
Original website title
GoogleCloudPlatform/asl-ml-immersion · Official homepage title · retained in its source language
How we review sourcesSuggest a correction