Google Cloud Platform Vertex AI end-to-end workflows for machine learning operations
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Updated
May 27, 2024 - Jupyter Notebook
Google Cloud Platform Vertex AI end-to-end workflows for machine learning operations
This is the repository of my study in MLOps Zoomcamp from DataTalksClub.
MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.
Advanced course on tools and practices aiming at designing, developing, deploying and maintain in production microservice architectures.
It's a repository for my progression on the online zoomcamp's MLOps course.
This Guidance demonstrates how to deploy a machine learning inference architecture on Amazon Elastic Kubernetes Service (Amazon EKS). It addresses the basic implementation requirements as well as ways you can pack thousands of unique PyTorch deep learning (DL) models into a scalable architecture and evaluate performance at scale
Pybind11 bindings for Whisper.cpp
AI book for everyone
A Production Tool for Embodied AI
🏕️ Reproducible development environment
This project showcases the concept of MLOps using a CI/CD pipeline, from gathering data, splitting the data, training the model, to building and deploying an application that provides predictions.
Model that classifies the news as Fake or Real and a complete MlOps Template for machine learning projects.
Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
Visit the MLOps Guide:
Very Basic package to store results of ML models Grid search results are hard to exploit. This package aims to store them in a more convenient way.
MLOps deploying house estimate model
Hera is a Python framework for constructing and submitting Argo Workflows.
End to end machine leanring project: This repository serves as a simplified guide to help you grasp the fundamentals of MLOps.
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