Final Project Submission: Build a Machine Learning Pipeline for Airfoil Noise Prediction

Primitives for machine learning and data science.

My personal notes, code and projects of the Udacity Data Science Nanodegree.

An open-source Python library for processing and developing End-to-End AI pipelines for Time Series Analysis

create a robust, simple, effecient, and modern end to end ML Batch Serving Pipeline Using set of modern open-source/free Platforms/Tools

Distributed Machine Learning Patterns from Manning Publications by Yuan Tang https://bit.ly/2RKv8Zo

Although PdM revolves around detecting anomalies and possible defects before they happen, here, however, based on befitting predictors and machine failures, this project attempts to predict Air Temperature that in actuality is generated using random walk process, via a regression algorithm.

sklearn pipeline

Example string processing pipeline on Triton Inference Server

ML AutoTrainer Engine, developed using Streamlit, is an advanced app designed to automate the machine learning workflow. It provides a user-friendly platform for data processing, model training, and prediction, enabling a seamless, code-free interaction for machine learning tasks.

Python library for Executable Machine Learning Knowledge Graphs

This Project is in collaboration with Figure Eight. The dataset contains pre-labelled tweets and messages from real-life disaster events. The project aim is to build a Natural Language Processing (NLP) model to categorize messages on a real time basis.

Machine Learning Pipelines Using Sklearn

Dicoding Submission Machine Learning Operations (MLOps) - Final project Diabetes Classification

Dicoding Submission Machine Learning Operations (MLOps) - First project Human Stress Prediction

based on the befitting sensors fetched data, prediction is to be made whether the failure in a vehicle is due to APS or some other component. Emphasis is on reducing the consequential cost by reducing the false positives and false negatives and more importantly false negatives as it appears cost incurred due to them is 50 times higher.

Machine Learning pipelines are deployed to accomplish the objective of credit risk analysis.

🌀 #11. "Machine Learning Operations (MLOps) - NLP"