This project focuses on building end-to-end machine learning pipeline using AWS SageMaker to predict the price range of mobile phones based on their specifications, enhancing consumer decision-making and streamlining the development process.

Proving Skills in Pipelines, Pickle Files and ML Modelling

A deployed machine learning model that has the capability to automatically classify the incoming disaster messages into related 36 categories. Project developed as a part of Udacity's Data Science Nanodegree program.

Big data application of Machine Learning concepts for sentiment classification of US Airlines tweets. The focus is on the usage of pyspark libraries (ml-lib) on big data to solve a problem using Machine Learning algorithms and not about the choice of algorithm used in the ML model creation. It also involves data pre-processing using NLP techniqu…

Collaborative team machine learning project classifying reviews scraped from the IMDB website as either positive or negative using sentiment classification. Tools used: BeautifulSoup and Splinter to scrape reviews, Pyspark, SQLAlchemy and Heroku.

Develop algorithms to classify genetic mutations based on clinical evidence (text).

In this project, I developed a completed Vertex and Kubeflow pipelines SDK to build and deploy an AutoML / BigQuery ML regression model for online predictions. Using this ML Pipeline, I was able to develop, deploy, and manage the production ML lifecycle efficiently and reliably.

This shows the machine learning pipeline for Classification and Clustering using Pycaret 3.0 on jupyter notebook

Course 2 project of the Udacity ML DevOps Nanodegree Program

This repository contains my code solution to DeepLearning.AIs Practical Data Science On AWS Cloud Specialization.

Learning create CI-CD for Machine Learning Pipelines Github Actions

Testing preprocessing capabilities of different ML libraries

ML pipeline to categorize emergency messages based on the needs communicated by the sender.

Website built in JavaScript & React as a "blog" to document an ML pipeline I built for Apartment Price Scraping project

This Project is a part of Data Science Nanodegree Program by Udacity in collaboration with Figure Eight. The initial dataset contains pre-labelled tweet and messages from real-life disasters. The aim of this project is to build a Natural Language Processing tool that categorize messages.

This a repo that was created to learn more about Airflow and develop awesome data engineering projects. 🚀🚀

Fraud detection ML pipeline and serving POC using Dask and hopeit.engine. Project created with nbdev: https://www.fast.ai/2019/12/02/nbdev/

Components that I have created for Kubeflow Pipelines. Try them in https://cloud-pipelines.net/pipeline-editor/

Open source AI platform for rapid development of advanced AI and AGI pipelines.

Best practices for engineering ML pipelines.