A Passionate Technologists' Journal

My Journey Building a Production-Ready ML Pipeline: House Rent Prediction How I learned to build a scalable machine learning system from scratch using Apache Spark, Kubernetes, and AWS services 🎯 The Learning Challenge When I started my ML learning journey, I wanted to build something real - not just another tutorial project. I decided to create a house rent prediction system that could actually be used in production. The challenge was daunting: I needed to build a system that could: Process thousands of house listings efficiently Train ML models with complex feature engineering Serve predictions in real-time Scale automatically based on demand Maintain data lineage and reproducibility ...

Note/ Warning: Am not going to go deeper/step-by-step into the deployment process and I'm not going to go deeper into the Pros & Cons by comparing them against each other, as well.  I just wanted to share my learnings/difficulties faced in the process of deploying my server app to Heroku, Amazon-AWS & Docker-Cloud. Heroku: This is very straight-forward where you have different options: Deploying with Git: Just like how you use Git to push code to your Git repo, you can push your Heroku app onto their server using the very similar commands. This is easiest/simplest way to deploy apps on Heroku. GitHub Integration: If your repo is hosted on GitHub, then this is like a piece of cake, you can simply automate your whole deployment process. Dropbox Sync: If you're not hosting your repo for any reason and just saving them into your Dropbox folder, then also you're covered with the Heroku's easiest Deployment process. You can simply connect your Heroku ac...