This repos contains notebooks for the Advanced Solutions Lab: ML Immersion

Notebooks, code samples, sample apps, and other resources that demonstrate how to use, develop and manage machine learning and generative AI workflows using Google Cloud Vertex AI.

A GitHub Action for running a Google Cloud Vertex AI notebook.

This repository compiles code samples and notebooks demonstrating how to use Generative AI on Google Cloud Vertex AI.

Jupyter Notebook Magics for Google Cloud ML Engine

Contains notebooks prepared for ML Bootcamp organized by Google Developers Launchpad.

Example of running Jupyter notebook on Google Cloud serverless products

JupyterLab Extension For Sharing Notebooks for GCP AI Platform

Dockerfile with Jupyter Machine Learning environment plus Google Cloud SDK

Working with Keras on GCP Notebook Instance

Notebooks for GCP services

The official notebooks are organized by Google Cloud Vertex AI products.

Labs and demo notebooks for MLOps course of Google Cloud Platform Specialization

This is a series of notebooks intended as a tutorial for deploying a Keras model (a Convolutional Neural Network for Fashion MNIST) on Google Cloud using AI Platform Prediction. It includes the steps on how to get predictions from the model from a local Python client. It assumes a Google Cloud Account with a project and bucket created, the Googl…

Summer 2018 Internship project with the Devito Group at Imperial College London. Jupyter Notebooks with code to perform Full Waveform Inversion using Devito with Tensorflow distributed over a Dask-Kubernetes Cluster hosted on Google's Kubernetes Engine on the Google Cloud Platform.

This AI Platform notebook guides you through the process of from building a k-means clustering models for market segmentation using BigQuery ML to evaluation using Davies-Bouldin index

Contains a Jupyter Notebook that focuses on creating an AutoML trained model using Google Cloud Platform's Vertex AI to predict how long a customer will engage with a video ad for

I created a Python script to visualize the weather of 500+ cities across the world of varying distance from the equator. Then, I used Jupyter notebook, Google Maps, and Google Places API, and created a heat map of humidity. Finally, I created my ideal weather condition on the map, used Google Places API to find the hotel information for each city.