This repo explore the vertex ai component
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Updated
Dec 11, 2023 - PureBasic
This repo explore the vertex ai component
Test code using Google Cloud Vertex AI.
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
Runs queries on the 59 million records in the BigQuery public dataset New York Citibike, in addition to making data visualizations on Google Cloud Platform (GCP), using Cloud SQL (MySQL), Vertex AI, Cloud Shell, and Cloud Storage buckets in Google Cloud Platform (GCP).
An end-to-end example of Chicago taxi on Google Cloud using TensorFlow, TFX, and Vertex AI
example of how to use VertexAI LLM in local development environment
Natural Language Processing
End to end machine learning models deployment to production for a bank marketing problem.
This Vertex AI Pipeline orchestrates the selection, deployment, and real-time monitoring of the highest-performing machine learning model from a pool of candidates. Designed to support a dynamic and collaborative model development environment, it ensures that only the most accurate and relevant models are deployed for fraud detection tasks.
A voice assistant that listens through your default microphone and responds through your output (speakers/headphones).
Tuning, training, and testing Keras models for CRNN handwritten text digitization.
Train, infer and deploy suitable machine learning models for any uploaded dataset, and visualise the experimental results (MLflow) easily from the GUI.
Machine Learning and NLP learning progress
A minimum Python package built on top of the LangChain framework to interact with LLM.
This repository showcases a Convolutional Neural Network (CNN) module developed on Jupyter and an AutoML module implemented on Google Cloud Platform (GCP) through VertexAI
Lambda function for making API calls to Google Gemini AI
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