Image by Author | Ideogram   #  Introduction   Docker has simplified how we develop, ship, and run applications by providing consistent environments across different systems. However, this consistency comes with a trade-off: debugging becomes deceptively complex for beginners when your applications — including Python applications — are running inside Docker containers. For those new…

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 | ChatGPT   #  Introduction   Feature engineering gets called the 'art' of data science for good reason — experienced data scientists develop this intuition for spotting meaningful features, but that knowledge is tough to share across teams. You'll often see junior data scientists spending hours brainstorming potential features, while senior folks end…

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 Editor | ChatGPT   #  Introduction   Machine learning has become an integral part of many companies, and businesses that don't utilize it risk being left behind. Given how critical models are in providing a competitive advantage, it's natural that many companies want to integrate them into their systems. There are many ways…

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…

Image by Author | ChatGPT   Introduction   Python's built-in datetime module can easily be considered the go-to library for handling date and time formatting and manipulation in the ecosystem. Most Python coders are familiar with creating datetime objects, formatting them into strings, and performing basic arithmetic. However, this powerful module, sometimes alongside related libraries…

Image by Author | Canva   If you like building machine learning models and experimenting with new stuff, that’s really cool — but to be honest, it only becomes useful to others once you make it available to them. For that, you need to serve it — expose it through a web API so that…