Management Dashboard for Torchserve
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Updated
Jan 31, 2023 - Python
Management Dashboard for Torchserve
Pushing Text To Speech models into production using torchserve, kubernetes and react web app 😄
Serving large ml models independently and asynchronously via message queue and kv-storage for communication with other services [EXPERIMENT]
An end-to-end Machine Learning project from writing a Jupyter notebook to check the viability of the solution, to breaking down the same into modular code, creating a Flask web app integrated with a HTML template to make a website interface, and deploying on AWS and Azure.
🔥🔥🔥🔥🧊🔥🔥 A Data Platform for Monitoring and Detecting Anomalies in Real-Time.
In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype…
A EKS-based ML deployment solution
Simply Automate Monitoring Infrastructure with Terraform, Ansible, AWS EC2, Nginx, Prometheus, Grafana and Github Actions 😄
Deployment of 3D-Detection and Tracking pipeline in simulation based on rosbags and real-time.
Powerful AutoML toolkit
This repo shows how to implement a simple image generation app that uses Jax-Implementation of a conditional VAE, Jax, fastapi, docker, streamlit, heroku, ec2, and cloudflare 😃
This project is part of the Udacity Azure ML Nanodegree. In this project, we use Azure to configure a cloud-based machine learning production model, deploy it, and consume it. We also create, publish, and consume a pipeline.
Identifying Patterns and Trends in Campus Placement Data using Machine Learning
A regression model to predict calories burnt using values from multiple sensors.
Base classes and utilities that are useful for deploying ML models.
A basic example of deploying machine learning applications
Ensemble Learning | Flask
Demonstration of building a machine learning model and deploying it on a web app.
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