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.

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]

Deployment of 3D-Detection and Tracking pipeline in simulation based on rosbags and real-time.

A EKS-based ML deployment solution

An ECS-based ML deployment solution

An implementation of seminal CVPR 2016 paper: "A Hierarchical Deep Temporal Model for Group Activity Recognition."

Base classes and utilities that are useful for deploying ML models.

A basic example of deploying machine learning applications

🌐 Language identification for Scandinavian languages

ml-deploy-lite is a Python library designed to simplify the deployment of machine learning models. It allows developers to quickly turn their models into REST APIs or gRPC services with minimal configuration. The library integrates seamlessly with Docker and Kubernetes, providing built-in monitoring and logging for performance and error tracking.

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.

We will apply deep learning techniques for the classification of the free-spoken-digit-dataset, akin to an audio version of MNIST.

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.

🪘 Tabla Drum Image Generator – AI-powered tabla drum image generation using Stable Diffusion & GANs. Features custom dataset curation, ML training pipeline, and scalable API deployment.

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.

A full-stack machine learning architecture for food delivery ETA prediction, leveraging a DVC-driven pipeline, automated CI/CD workflows, cloud artifact management, and LGBM-based stacked regression ensemble for high-fidelity time estimations.

Terraform code, aws scripts and pipeline templates for the AWS-IaC-mlops-pipeline.

A Streamlit-based churn prediction app using a trained Random Forest model to analyze customer behavior and predict churn based on demographics, spending, interaction history, and service usage.