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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.
Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework
Python based framework for Automatic AI for Regression and Classification over numerical data. Performs model search, hyper-parameter tuning, and high-quality Jupyter Notebook code generation.
This is the Capstone project (last of the three projects) required for fulfillment of the Nanodegree Machine Learning Engineer with Microsoft Azure from Udacity. In this project, we use a dataset external to Azure ML ecosystem. Azure Machine Learning Service and Jupyter Notebook is used to train models using both Hyperdrive and Auto ML and then …
In this project, we are using the Bank Marketing dataset to create a cloud-based machine learning production model and a pipeline on Azure Machine Learning. We will create a model with Auto Machine Learning, deploy it, and consume it
Toward Automated Continual Learning (for fully auto-adaptive learning methods and systems). Here, is a list of materials useful to realize this project.
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
This notebook is designed to interactively guide the user through an end-to-end process for deploying an automated machine learning workflow utilizing h2o.ai's autoML function. The user is simply required to select a dataset and choose a variable they would like to predict before running the automation. The user can choose to run the automation …