Data Science and Databases

TensorFlow is one of the leading tools for training deep learning models. Outside that space, it may seem intimidating and unnecessary, but it has many creative uses—like producing highly effective adversarial input for black-box AI systems.

Web scraping has been around since the early days of the World Wide Web, but scraping modern sites that heavily rely on new technologies is anything but straightforward.

In this article, Toptal Software Developer Neal Barnett demonstrates how you can use Python and Selenium to scrape sites that employ a lot of JavaScript, iframes, and certificates.

Properly used, an SQL database index can be so effective that it might seem like magic. But the following series of exercises will show that underneath, the logic of most SQL indexes—and wielding them correctly—is quite straightforward.

ETL can consolidate data from various sources into an organized, reliable, and usable database. This allows businesses to employ previously unused or underused data to improve their performance.

In this article, Toptal Data Modeling Developer Alexandre Wanderer demonstrates all stages of the ETL process in building a data warehouse.

Pre-trained models are making waves in the deep learning world. Using massive pre-training datasets, these NLP models bring previously unheard-of feats of AI within the reach of app developers.

For those working with AI, the future is certainly exciting. At the same time, there is a general sense that AI suffers from one pesky flaw: AI in its current state can be unpredictably unreliable.

One of the new possibilities offered by Lambda@Edge is the ability to implement server-side A/B testing using Lambdas on CloudFront’s edge servers.

In this article, Toptal Full-stack Developer Georgios Boutsioukis guides you through the process and outlines the pros and cons of A/B testing with Lambda@Edge.