Operations | Monitoring | ITSM | DevOps | Cloud


From MLOps to LLMOps: The evolution of automation for AI-powered applications

Machine learning operations (MLOps) has become the backbone of efficient artificial intelligence (AI) development. Blending ML with development and operations best practices, MLOps streamlines deploying ML models via continuous testing, updating, and monitoring. But as ML and AI use cases continue to expand, a need arises for specialized tools and best practices to handle the particular conditions of complex AI apps — like those using large language models (LLMs).

What is an IDE?

An IDE (integrated development environment) is software that combines all the functions needed for development in one place. Without an IDE, developers would need to use both a text editor to enter code and a separate compiler to make the program understandable to the computer. An IDE combines these features into one tool, making development more efficient.

Splitting and parallelizing Android UI tests with Espresso and CircleCI

For Android developers, test automation on CI/CD platforms such as CircleCI has become an indispensable part of the development workflow. But merely implementing automated testing is no longer enough to remain competitive and continue to develop at speed. Developers must also work to continuously monitor, maintain, and improve their test automation. As an application grows in complexity, the scale of development grows, as does the number of automated tests.

Achieving AI development at scale ft. Luis Ceze of OctoAI

In this episode, Rob is joined by Luis Ceze, CEO of OctoAI and a distinguished professor of computer science at the University of Washington. Together, they unpack the surge of interest in AI, attributing it to the convergence of factors like the unprecedented availability of data thanks to the internet boom and the accessibility of powerful computing resources.