Towards Data Science

The production cycle in machine learning refers to the process of taking a model from development to deployment. This process involves several stages, including data collection, preprocessing, model training, model evaluation, and deployment. Data Collection Data collection is the first stage of the production cycle. In this stage, we collect data from…

Introduction The field of machine learning is rapidly evolving, and professionals have the option to work in research or production. While both fields have their unique challenges and rewards, there are significant differences between the two. Working in Research Research in machine learning involves exploring new techniques and developing new algorithms. Researchers often work…

Research plays a crucial role in the development of machine learning technology. With the advancement of machine learning, the research community has adopted a “bigger, better” approach, requiring a massive amount of data and computing power, which costs tens of millions of dollars. …

Did you know that Power BI relies on two different cache types? In this article, we’ll demystify how each of them works in real life — How many times did you find yourself in the following situation? When you open the report for the first time, it takes a while to render, but once you go back and forth from other report pages, that same page renders significantly faster! Yeah, I know, we’ve all been there…

A hands-on article on detecting outliers in time series data using Python and sklearn — Anomaly detection is a task in which we want to identify rare events that deviate significantly from the majority of the data. Anomaly detection in time series has a wide range of real-life applications, from manufacturing to healthcare. Anomalies indicate unexpected events, and they can be caused by production faults…