Learn Data Science With Mukeshram

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 Feature Engineering, I quickly realized there was a gap between theory and what actually happens in practice. This post is about what i learned about feature engineering that no tutorial tells you. 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 Feature Engineering As I reflect on my journey in machine learning, I've come to realize that feature engineering is often the unsung hero of a successful model. It's easy to get caught up in the latest algorithms and techniques, but at the end of the day, good features matter more than a fancy model. I've learned this the hard way, through trial and error, and I'm excited to share my experiences with you. One of the most important lessons I've learned is that domain knowledge beats any automated feature selection algorithm. There's no substitute for understandin...

When I started working with GitHub Actions, I quickly realized there was a gap between theory and what actually happens in practice. This post is about how i set up a ci/cd pipeline for ml models using github actions. 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 CI/CD Pipelines for ML Models As I delved into the world of Machine Learning Operations (MLOps), I quickly realized the importance of implementing a robust Continuous Integration/Continuous Deployment (CI/CD) pipeline. This wasn't just about automating repetitive tasks; it was about ensuring the reliability and consistency of our ML models. In this article, I'll share my experience of setting up a CI/CD pipeline using GitHub Actions, highlighting the lessons I learned, the challenges I faced, and the solutions I discovered. Getting Started with GitHub Actions One of the primary reasons...

When I started working with Monitoring, I quickly realized there was a gap between theory and what actually happens in practice. This post is about why model monitoring matters more than model training. 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 the Importance of Model Monitoring As I delved into the world of machine learning operations (MLOps), I quickly learned that the real challenge lies not in training a model, but in ensuring it continues to perform well after deployment. I've seen firsthand how a model that excels in testing can silently deteriorate in production, often due to data drift - a change in the distribution of input data that can render a model ineffective. My experiences have taught me that model monitoring is not just a nice-to-have, but a critical component of any successful MLOps strategy. The Dangers of Assuming Deployment ...

When I started working with ML, I quickly realized there was a gap between theory and what actually happens in practice. This post is about mistakes i made while training my first ml model. 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 Mistakes I still remember the excitement of training my first machine learning model. I had spent weeks collecting and preprocessing the data, and finally, it was time to see the results. But, as it often does, reality had other plans. My model's performance was suspiciously high, and it wasn't until later that I realized the mistakes I had made. In this post, I'll share the lessons I learned from those mistakes, in the hopes that you can avoid them in your own machine learning journey. My Experience with Machine Learning As a beginner, I made a few critical errors that affected my model'...

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 ...