Operations | Monitoring | ITSM | DevOps | Cloud

In Large Language Model (LLM) applications, the quality of the training data is paramount in determining the final model performance. One of the most important steps in preparing datasets is cleaning and transforming raw data into similar and usable formats. However, this process can be tedious and time-consuming when done manually. Automating these data cleaning workflows is essential to improve efficiency and maintain consistency across multiple datasets.

Cloud environment cleanup is often overlooked but it’s one of the biggest blockers to efficient cloud transformation. Have you ever found yourself lost in a labyrinth of environment names? “Dev,” “development,” “dev-test” — sound familiar? What starts as a simple spreadsheet import can quickly spiral into a nightmare of duplicate records and data inconsistencies. One of our customers knows this pain all too well.

As part of Atlassian’s ongoing investment in security, we’re excited to introduce token rotation for access tokens in Bitbucket Cloud. Building on recent updates, like adding expiration dates to access tokens, this new capability allows you to rotate your tokens, which generates a new secret while maintaining the same access and scopes.