Learn Data Science With Mukeshram

When I started working with PostgreSQL, I quickly realized there was a gap between theory and what actually happens in practice. This post is about postgresql for ml engineers - storing features, predictions, and logs. I'll walk you through what I learned, what tripped me up, and the lessons that stuck with me. No fluff — just honest notes from someone who went through it. Introduction to PostgreSQL for ML Engineers As I delved into the world of machine learning (ML) engineering, I quickly realized the importance of a robust database management system. PostgreSQL, with its powerful features and flexibility, became my go-to choice for storing and managing ML-related data. In this article, I'll share my experiences, mistakes, and lessons learned from using PostgreSQL in ML projects, highlighting the benefits of using this database system for storing features, predictions, and logs. The Importance of Auditing and Debugging One of the most significant advantages of using a dat...

When I started working with Project Structure, I quickly realized there was a gap between theory and what actually happens in practice. This post is about how i structured a real ml project from scratch. I'll walk you through what I learned, what tripped me up, and the lessons that stuck with me. No fluff — just honest notes from someone who went through it. Introduction to Machine Learning Project Structure I'll never forget my first machine learning project. I was excited to dive in and start building, but I made a critical mistake: I put all my code in a single, massive script. It wasn't long before I realized that this approach wouldn't scale, especially when I added a second teammate to the project. The script was cumbersome, difficult to navigate, and prone to errors. I learned the hard way that a well-structured project is essential for success in machine learning. As I worked through the challenges of building a machine learning project from scratch, I disco...

When I started working with Celery, I quickly realized there was a gap between theory and what actually happens in practice. This post is about running background tasks in python with celery and redis. I'll walk you through what I learned, what tripped me up, and the lessons that stuck with me. No fluff — just honest notes from someone who went through it. Introduction to Background Tasks As a developer, I've often found myself dealing with tasks that are too heavy to be handled within the request cycle of my web application. Whether it's sending emails, processing large datasets, or making API calls, these tasks can significantly slow down my application's response time. That's where Celery comes in – a distributed task queue that allows me to run background tasks asynchronously. In this article, I'll share my experience with Celery and Redis, highlighting the lessons I've learned and the challenges I've faced. Why Celery and Redis? I chose Celery ...

When I started working with MLflow, I quickly realized there was a gap between theory and what actually happens in practice. This post is about how mlflow changed the way i track ml experiments. I'll walk you through what I learned, what tripped me up, and the lessons that stuck with me. No fluff — just honest notes from someone who went through it. Introduction to MLflow As I delved into the world of machine learning, I quickly realized that tracking experiments was a crucial part of the development process. However, I was doing it the hard way - saving metrics in print statements and notebooks. It wasn't until I discovered MLflow that I was able to streamline my workflow and make the most out of my experiments. In this article, I'll share my experience with MLflow, the lessons I learned, and how it changed the way I approach ML development. The Struggle is Real Before MLflow, I was struggling to keep track of my experiments. I would run multiple iterations, tweaking ...

Introduction Python is one of the most popular programming languages used in data science. Many beginners feel afraid when they hear the word  “programming,” but Python is simple and easy to learn In this post, I’ll explain Python basics that every data science beginner should understand before moving to advanced topics. Why Python Is Used in Data Science Python is widely used because: • It is easy to read and understand • It has many data science libraries • It is beginner-friendly • It supports automation and analysis Because of these reasons, Python is the first language recommended for data science students Basic Python Concepts You Should Learn First Before moving to data science libraries, you should understand these basics. 1. Variables Variables are used to store values. Example: x = 10 name = "Data Science" 2. Data Types Common data types include: • Integer (numbers) • Float (decimal values) • String (text) • Boolean (True or False) Understanding data types helps avo...