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Showing posts with the label MachineLearningModel

Fine-tuning an LLM

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Fine-tuning TinyLlama Locally I recently fine-tuned TinyLlama on a small custom dataset and was impressed by how well it learned the specific response style. Here's what I did and the results. You can try it out yourself by checking out the repository . What is Fine-tuning? Fine-tuning takes a pre-trained language model (one that already understands general language) and trains it further on specific data to improve performance on particular tasks. Think of it as giving a general-purpose assistant specialized training in a specific domain. The Training Data I started with just 3 examples in a simple JSON format: [ {"prompt": "Explain Python lists", "response": "Python lists are ordered, mutable collections."}, {"prompt": "What is a dictionary?", "response": "A dictionary stores key-value pairs with fast lookup."}, {"prompt": "Explain list comprehension", ...

My Journey Building a Production-Ready ML Pipeline: House Rent Prediction

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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 ...

Evolution of AI: From ML to ANI/AGI/ASI - A Sci-Fi Inspired Journey

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Optimizing Inventory Management with the Inventory Optimizer Tool

Efficient inventory management is a critical component of running a successful business. Managing stock levels, forecasting demand, and ensuring timely reorders can make or break a company’s ability to meet customer demand while keeping costs down. That's where an inventory optimizer comes in! This blog post introduces a Python-based Inventory Optimizer Tool designed to help businesses manage inventory more effectively. We’ll walk through its key features, show you how it works, and explain how even non-technical users can benefit from it. Why Inventory Management Matters Poor inventory management can lead to several problems: - Overstocking : Holding too much inventory leads to excess costs and storage issues. - Stockouts:  Running out of products can result in lost sales and unhappy customers. - Inefficient Reorders : Not having a proper reorder process can lead to delayed shipments and rushed orders, increasing costs. Our Inventory Optimizer Tool  uses simple data to forec...