Image by Author   Data science projects are notorious for their complex dependencies, version conflicts, and "it works on my machine" problems. One day your model runs perfectly on your local setup, and the next day a colleague can't reproduce your results because they have different Python versions, missing libraries, or incompatible system configurations. This…

Image by Author   #  Introduction   Stress testing is crucial for understanding how your application behaves under heavy load. For machine learning-powered APIs, it is especially important because model inference can be CPU-intensive. By simulating a large number of users, we can identify performance bottlenecks, determine the capacity of our system, and ensure reliability.…

Image by Author | Ideogram   #  Introduction   Picture this: you're working on a Python project, and every time you want to run tests, you type python3 -m pytest tests/ --verbose --cov=src. When you want to format your code, it's black . && isort .. For linting, you run flake8 src tests. Before you…

Sponsored Content       Web data has become a key resource for businesses, whether you're running a startup or working at a Fortune 500 company. With the market projected to grow at a 13.2% CAGR through 2036, more companies are turning to web scraping solutions to collect and analyze data efficiently. In this…

Image by Author | Canva   Ever run a Python script and immediately wished you hadn’t pressed Enter? Debugging in data science is not just an act; it’s a survival skill — particularly when dealing with messy datasets or devising prediction models on which actual people rely. In this article, we will explore the basics…

Image by Editor   In data science and machine learning, raw data is rarely suitable for direct consumption by algorithms. Transforming this data into meaningful, structured inputs that models can learn from is an essential step — this process is known as feature engineering. Feature engineering can impact model performance, sometimes even more than the…