Browsing: Teams

Every year, we look forward to GitHub’s State of the Octoverse report. It is one of the best leading indicators of where software development is heading, and what the rest of the technology industry can expect in the coming years. This year’s report is especially exciting, and the numbers are staggering.

With the launch of AWS Batch support for Amazon SageMaker Training jobs, customers gained a new fully managed way to queue, schedule, and retry machine learning (ML) training workloads without building their own infrastructure. Instead, they use the queuing and scheduling features of AWS Batch. As organizations scale their artificial intelligence and machine learning (AI/ML)…

Multiple teams within the same company increasingly need shared access to expensive GPU clusters for their generative AI operations, while maintaining isolation boundaries, resource fairness, and operational independence. Consider a data science team training large language models, a computer vision group running inference workloads, and a research team experimenting with new model architectures. All of…