Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
MLOps for DevOps Engineers - A hands-on, project-based guide to Machine Learning Operations
Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
A Tiny Intent Classifier model for short customer-support style text. Given an input text (e.g., "Hi, I need help with my bill"), the model returns one of: `greeting` - `question` - `complaint` - `praise` - `other`.
End to end machine leanring project: This repository serves as a simplified guide to help you grasp the fundamentals of MLOps.
An end-to-end MLOps pipeline(CI/CD/CT/CM) project for training, versioning, deploying, and monitoring machine learning models using FastAPI, Kubernetes, MLflow, DVC, Prometheus, and Grafana.
The project is a concoction of research (audio signal processing, keyword spotting, ASR), development (audio data processing, deep neural network training, evaluation) and deployment (building model artifacts, web app development, docker, cloud PaaS) by integrating CI/CD pipelines with automated tests and releases.
The Machine Learning Zoomcamp teaches foundational and advanced ML concepts using tools like NumPy, Pandas, Scikit-Learn, TensorFlow, XGBoost, Flask, Docker, AWS, Kubernetes, and KServe. It covers regression, classification, evaluation metrics, neural networks, deployment strategies, and end-to-end projects to bridge theory and practice.
Consignment-Price Prediction project aims to develop a machine learning model that can accurately predict the price of consignment items based on various features and variables
MLOps deploying house estimate model
Automated pipeline for energy consumption forecasting across Europe using Azure cloud and Databricks.
Predictive maintenance can help companies minimize downtime, reduce repair costs, and improve operational efficiency. Developing a web application for predictive maintenance can provide users with real-time insights into equipment performance, enabling proactive maintenance, and reducing unplanned downtime.
A complete pipeline for sentiment analysis using Hugging Face Transformers and AWS services. The model can be run on both Streamlit Share Server and AWS (using S3 for storage and EC2 for deployment). This repository covers data preprocessing, model training, evaluation, and accurate sentiment prediction on reviews.
End-to-end MLOps project for predictive maintenance using engine sensor data. Includes data versioning on Hugging Face, MLflow experiment tracking, CI/CD with GitHub Actions, and Dockerized Streamlit deployment for real-time engine failure classification.
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 😃
CI/CD ( Continous Deploy) With Github Actions, Docker & Docker Compose
Implementation of classification of grammatically correct sentences and wrong sentences, and integration of MLOps tools.
Full-stack app for generating subject-specific images using FLUX, with a FastAPI backend and a Vite + React + Tailwind frontend.
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