An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collection, ensuring safety & robustness. 📈

An open-source ML pipeline development platform

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

An AutoML pipeline selection system to quickly select a promising pipeline for a new dataset.

Free Open-source ML observability course for data scientists and ML engineers. Learn how to monitor and debug your ML models in production.

Free and open source automation platform

Best practices for engineering ML pipelines.

Library for streaming data and incremental learning algorithms.

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

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/

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…

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

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

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.

Proving Skills in Pipelines, Pickle Files and ML Modelling

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.

Example solution to the MLOps Case Study covering both online and batch processing.