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Notebooks, code samples and reference for machine learning and generative ai on Google Cloud for the Advanced Solutions Lab (ASL) bootcamps.
| Date | Stars |
|---|---|
| 2026-07-24 | 2585 |
| 2026-07-25 | 2585 |
| 2026-07-28 | 2585 |
| 2026-07-30 | 2585 |
| 2026-07-31 | 2588 |
| 2026-08-06 | 2591 |
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Momentum
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# Advanced Solutions Lab ## Overview This repository contains AI and Machine Learning contents meant to be run on Google Cloud. This is maintained by Google Cloud’s [Advanced Solutions Lab (ASL)](https://cloud.google.com/asl) team. This repository contains 3 main modules to covers various AI/ML toipcs: - `asl_core`: A wide range of model architectures (DNN, CNN, RNN, transformers, SNGP, etc.) targeting many data modalities (tabular, image, text, time-series) implemented mainly in Tensorflow and Keras. - `asl_mlops`: Tools on Google Cloud’s Agent Platform for operationalizing Tensorflow, Scikit-learn and PyTorch models at scale (e.g. Agent Platform training, tuning, and serving and MLOps on Kubeflow pipelines). - `asl_genai`: Generative AI and Agent System using Gemini and Agentic Frameworks like Google ADK. ## Repository Structure Each module (`asl_core`, `asl_mlops`, `asl_genai`) has separate environment and materials, which are organized in each directory. All learning materials are in the contets folder. This folder is organized by different topics. Each folder contains a `labs` and a `solutions` folder. Use the `labs` notebooks to test your coding skills by filling in TODOs and refer to the notebooks in the `solutions` folder to verify your code. We have three main folders described below: ``` ├── asl_core │ ├── notebooks - contains learning materials organized by topic │ │ ├── building_production_ml_systems │ │ │ ├── labs │ │ │ └── solutions │ │ ├── end-to-end-structured │ │ ├── image_models │ │ ├── ... │ ├── kernels - contains kernel scripts needed for certain notebooks │ ├── scaffolds - contains sample code to accelerate AI/ML projects │ ├── requirements.txt - dependencies for this module ├── asl_mlops │ ├── ... ├── asl_genai │ ├── ... ├── ... ``` ## Environment Setup ### Step 1. Run the Setup Script on Cloud Shell This repository is tested on Agent Platform Workbench and Cloud Workstations. To begin, run the setup script in [Cloud Shell](https://shell.cloud.google.com) to configure essential project infrastructure (APIs, IAM, Buckets). Run the setup script in [Cloud Shell](https://shell.cloud.google.com) to provision your environment. ```bash git clone https://github.com/GoogleCloudPlatform/asl-ml-immersion.git cd asl-ml-immersion bash scripts/setup_env.sh ``` You will be prompted to select the environment to set up: * **1) Agent Platform Workbench:** Setup Agent Platform Workbench. * **2) Cloud Workstations:** Setup Cloud Workstations. * **3) Setup both:** Setup both environments. * **4) Skip:** Setup project infrastructure (APIs, IAM, Buckets) only. By selecting the option 1-3, you can automatically setup the environment, or you can select 4 and manually set up the environment following the official documentation: * **Agent Platform Workbench:** [Create a user-managed notebook instance](https://docs.cloud.google.com/gemini-enterprise-agent-platform/notebooks/workbench/instances/create-console-quickstart) * **Cloud Workstations:** [Create a workstation](https://docs.cloud.google.com/workstations/docs/create-workstation) Next, you will be asked if you want to attach a GPU (Nvidia T4) to the environment. Select `y` or `n` depending on your preference. **Note:** Accelerators (GPU/TPU) are not required in most of the notebooks, but some notebooks recommend using them. ### Step 2. Build the Environemnt Once your environment is running, open it. Then, run the commands below in Terminal **inside the environment** to clone this repository, and build the environemnt (venvs and jupyter kernels). ```bash git clone https://github.com/GoogleCloudPlatform/asl-ml-immersion.git cd asl-ml-immersion make ``` On Cloud Workstations, click `Open Folder` -> `asl-ml-immersion` to open the repository window. If the folder is already opend, `Command + Shift + P` and type `Developer: Reload Window` to reflect the changes. ## Using the Environment ### Running a notebook After t
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5befd40d6a356afe, topic:agents, readme:agentic
matched fp:5befd40d6a356afe, topic:tensorflow