Open source AI platform for rapid development of advanced AI and AGI pipelines.
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Updated
Jun 4, 2024 - JavaScript
Open source AI platform for rapid development of advanced AI and AGI pipelines.
Learning create CI-CD for Machine Learning Pipelines Github Actions
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. 📈
Website built in JavaScript & React as a "blog" to document an ML pipeline I built for Apartment Price Scraping project
Free and open source automation platform
ML pipeline to categorize emergency messages based on the needs communicated by the sender.
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.
Testing preprocessing capabilities of different ML libraries
An open-source ML pipeline development platform
Proving Skills in Pipelines, Pickle Files and ML Modelling
Library for streaming data and incremental learning algorithms.
Free Open-source ML observability course for data scientists and ML engineers. Learn how to monitor and debug your ML models in production.
This a repo that was created to learn more about Airflow and develop awesome data engineering projects. 🚀🚀
This repository contains my code solution to DeepLearning.AIs Practical Data Science On AWS Cloud Specialization.
Develop algorithms to classify genetic mutations based on clinical evidence (text).
This shows the machine learning pipeline for Classification and Clustering using Pycaret 3.0 on jupyter notebook
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/
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.
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…
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