Management Dashboard for Torchserve
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Updated
Jan 31, 2023 - Python
Management Dashboard for Torchserve
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 EKS-based ML deployment solution
Pushing Text To Speech models into production using torchserve, kubernetes and react web app 😄
A basic example of deploying machine learning applications
Base classes and utilities that are useful for deploying ML models.
Serving large ml models independently and asynchronously via message queue and kv-storage for communication with other services [EXPERIMENT]
Terraform code, aws scripts and pipeline templates for the AWS-IaC-mlops-pipeline.
An end-to-end ML project, which aims at developing a regression model for the problem of predicting the sales of a given product, based on its properties like item category, weight, visibility, MRP, type of outlet the product is sold, size of the outlet etc.
Deployment of 3D-Detection and Tracking pipeline in simulation based on rosbags and real-time.
This Flask web application performs text sentiment analysis and text generation based on user input. Users can input text, and the application will analyze its sentiment using NLTK's Vader sentiment analysis tool and generate additional text using the GPT-2 model.
Code Snippets for an Image Classification model deployed using FastAPI and Streamlit.
An end-to-end ML model deployment pipeline on GCP: train in Cloud Shell, containerize with Docker, push to Artifact Registry, deploy on GKE, and build a basic frontend to interact through exposed endpoints. This showcases the benefits of containerized deployments, centralized image management, and automated orchestration using GCP tools.
A classification model built to determine the issues in system given data from multiple sensors.
🌐 Language identification for Scandinavian languages
We will apply deep learning techniques for the classification of the free-spoken-digit-dataset, akin to an audio version of MNIST.
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