Operations | Monitoring | ITSM | DevOps | Cloud

One of the oldest (but often neglected) security vulnerabilities is SQL injection. One common scenario goes like this: An unsuspecting programmer writes an application that accepts input from the user which serves as a parameter to retrieve or store data from a database (e.g., a web login form). The programmer writes a dynamically populated SQL query inside the app, based on user input like username and password (see Image 1 for reference).

Many data science discussions focus on model development. But as any data scientist will tell you, this is only a small—and often relatively quick—part of the data science pipeline. An important, but often overlooked, component of model stewardship is monitoring models once they’ve been released to the wild. Here we’ll aim to convince any unbelievers that monitoring deployed models is as important as any other task in the data science workflow.

Monitoring an Azure environment can be a challenging task for even the most experienced and skilled team. Applications deployed on Azure are built on top of an architecture that is distributed and extremely dynamic. But all is not doom and gloom. Azure users have a variety of tools they can use to overcome the different challenges involved in monitoring their stack, helping them gain insight into the different components of their apps and troubleshoot issues when they occur.

AWS CloudWatch Logs gives you full visibility into your AWS infrastructure, from individual workloads to the services that bind them. Monitoring these logs helps ensure their smooth and continued operation, ongoing stability, and performance. Integrating CloudWatch Logs with LogDNA makes it easier to parse, search, and analyze AWS logs in order to detect anomalies and troubleshoot problems faster.