An open-source end-to-End ML ops controller and collaboration interface built on Google Cloud.
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Updated
Apr 5, 2023 - Python
An open-source end-to-End ML ops controller and collaboration interface built on Google Cloud.
This repo explore the vertex ai component
AI Assistant Discord Bot using Google Cloud Vertex AI Chat Bison model using Python
A quick and easy way to send request to the gemini-pro and gemini-pro-vision LLM API
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
3D CNN based video classification android application. Transcribes lip movements of the speaker in a silent video to text. The neural network captures spatio temporal information from video required to generate words from video. MLOps using Vertex AI was used to deploy the model in a CI/CD fashion on android app
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.
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