AI/ML & Data Engineering
Study notes and engineering content on machine learning fundamentals, NLP, data pipelines, model training, GPU compute, distributed processing, feature engineering, and practical ML workflows. Suitable for students and professionals exploring applied AI and Big Data systems.
The project outlines the creation of an AI-based examiner tool that automates the marking of handwritten GCSE exams. Teachers can upload scanned PDFs, and in 20-30 seconds, receive detailed feedback reports formatted as .docx files. Built using Python, Flask, and Gemini AI, it offers an efficient marking solution while ensuring data privacy.
The blog discusses the integration of Amazon EventBridge to automate AWS Glue workflows every two minutes, enhancing operational efficiency in data engineering and machine learning tasks. It details steps to create and configure EventBridge rules, set permissions, and verify workflows, emphasizing improvements in responsiveness, agility, and DataOps maturity.
The implemented stages of ingestion, preprocessing, EDA, and feature engineering have transitioned to automation and monitoring, forming a cohesive DataOps layer. By introducing orchestration, the independent Glue jobs become an automated, reliable workflow. Testing confirmed successful execution, paving the way for regular automations to enhance operations and insights from data.
The post discusses the evolution of a data pipeline, highlighting the integration of an API-driven layer for enhanced observability. This new functionality allows authorized users to access real-time operational status without manual checks across AWS services. The approach improves transparency, accountability, and agility while enabling proactive monitoring and automated responses in future enhancements.
This post details the development of a Machine Learning Pipeline for demand forecasting. Utilizing AWS Glue and PySpark, it covers training and evaluating Linear Regression and Random Forest models using an engineered feature dataset. Results show Random Forest slightly outperforms Linear Regression, demonstrating effective model stability and reliability for deployment.
The Feature Engineering stage follows Exploratory Data Analysis, preparing the dataset for machine learning. It generates temporal and statistical features, encodes categorical identifiers, and ensures schema consistency. Implemented in AWS Glue, it enables reproducibility and scalability for model training, enhancing forecasting accuracy by incorporating lag and rolling average features.
This article expands on a previous post about building a serverless ETL pipeline on AWS by focusing on Exploratory Data Analysis (EDA). It details how to establish the EDA environment using AWS Glue and PySpark after cleaning the dataset. Key insights include sales trends, store and item performance, and correlation analysis, laying the groundwork for a demand forecasting model.
The post details enhancements made to a serverless ETL pipeline using AWS Glue and PySpark for retail sales data. Improvements include explicit column type conversions, missing value imputation, normalization of sales data, and integration of logging for observability. These changes aim to create a production-ready, machine-learning-friendly preprocessing layer for effective data analysis.
This project aims to develop a demand forecasting solution for retail using historical sales data from Kaggle. A data pipeline employing AWS Glue and PySpark will preprocess the data by cleaning and splitting it into training and testing sets. The objective is to maximize inventory management and customer satisfaction.
Last weekend, I explored GPU CUDA programming using AWS. Despite initial service quota issues, I successfully launched an EC2 instance equipped with an NVIDIA GPU. After setting up the environment, I compiled and ran a CUDA program, achieving a remarkable speedup of 151 times faster on the GPU compared to the CPU.
The author explores CUDA programming on AWS using an NVIDIA GPU, facing vCPU quota limitations that prevent launching an EC2 instance. After diagnosing the issue, they submitted a request for a quota increase through the Service Quotas console. The experience highlights the importance of checking AWS service limits when setting up GPU instances.
The article discusses the challenges of consistent application behavior in software development and how Docker addresses these issues. It outlines the creation of a simple Flask app, its containerization using Docker, and steps to ensure accessibility from outside the container. Troubleshooting and cleanup procedures are also covered, emphasizing a portable setup.
APIs are the backbone of modern applications. Among the different approaches to building them, one of the most widely used is the RESTful API. In…
Continue reading → Mastering RESTful API Basics for Developers
In this blog, we’ll build a simple distributed file service on AWS. The setup will have two file servers — Server 1 and Server 2.…
Continue reading → Setting Up a Simple Distributed File Service on AWS
Aviation data has always been fascinating. Planes crisscross the globe. Each one sends out tiny bursts of information as it soars through the sky. Thanks…
Hadoop’s MapReduce framework is a powerful tool for processing large-scale data in a distributed fashion. In this guide, we walk through setting up a Hadoop…
In today's interconnected world, real-time location tracking plays a crucial role in many industries. These include logistics and fleet management. It is also vital to…
Continue reading → Building a Real-Time GPS Data Processing System on AWS: A Step-by-Step Guide