The goal of this project is to build a data driven model that finds the customer groups that lead to good ROIs (Return on Investment).
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Updated
Feb 4, 2023 - Jupyter Notebook
The goal of this project is to build a data driven model that finds the customer groups that lead to good ROIs (Return on Investment).
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.
It is a website that utilize machine learning model to predict the probability of getting placed and salary.
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.
In this project we use Microsoft Azure Cloud Computing Services to configure a cloud-based machine learning production model, deploy it, and consume it. We will also create, publish, and consume a pipeline.
Slides for ML deployment and MLOps
Terraform code, aws scripts and pipeline templates for the AWS-IaC-mlops-pipeline.
A classification model built to determine the issues in system given data from multiple sensors.
We will apply deep learning techniques for the classification of the free-spoken-digit-dataset, akin to an audio version of MNIST.
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.
A web app to showcase some of my favorite projects
This is Mudit Vyas worked as ML Developer Intern , Team Leader in Technocolabs Software.This is Internship Project For Technocolabs Software.
🌐 Language identification for Scandinavian languages
Base classes and utilities that are useful for deploying ML models.
Identifying Patterns and Trends in Campus Placement Data using Machine Learning
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.
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