Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.

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.

Automated pipeline for energy consumption forecasting across Europe using Azure cloud and Databricks.

Human Pose Classifier using Vision Transformers (ViT) – end-to-end pipeline for preprocessing, training, testing, and deploying models with FastAPI/Streamlit and AWS integration.

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.

Testing out ClearML.

Using MLflow to deploy your RAG pipeline, using LLamaIndex, Langchain and Ollama/HuggingfaceLLMs/Groq

An application for violent threat detection

A pipeline using Kedro to orchestrate the deployment of a deep learning transformer model for classifying toxic comments. This project integrates data preprocessing, model training, and deployment into a streamlined and reproducible workflow, enabling efficient handling of the toxic comment classification problem in NLP.

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`.

MLOps Loan Approval Prediction System

Anomaly detection in transactions means identifying unusual or unexpected patterns within transactions or related activities. These patterns, known as anomalies or outliers, deviate significantly from the expected norm and could indicate irregular or fraudulent behaviour.

This web application utilizes cutting-edge artificial intelligence to help people understand their risk of developing kidney disease. Developed with user-friendliness in mind, this tool allows individuals to easily enter their information and receive a personalized risk assessment.

A project to explore and learn about ZenML and mlflow using the hymenoptera dataset from PyTorch

In this repository, I guide you through deploying a Machine Learning project, specifically the Loan Approval Classifier, on Azure Cloud. Explore the entire process, from building the classifier codebase to seamless deployment. Dive into comprehensive steps, leveraging Azure Cloud for a robust machine learning solution. Let's empower your projects .

Testing ZenML

ETL Pipeline best practices implemented in this repository. Also check out the experiments tab on Dagshub

Customer Churn MLOps is an end-to-end machine learning pipeline for predicting customer churn using tabular data. It integrates DVC for data/model versioning, MLflow for experiment tracking, FastAPI for model serving, and GitHub Actions for CI/CD automation, making the project fully production-ready.

Testing flyte