Enterprises across numerous industries continue to see the value of AI. Whether it is to automate tasks, optimize business processes, detect fraud or make more informed decisions based on forecasting, enterprises are looking for ways to profit off AI and machine learning technology. To best optimize the power of AI, organizations must use the right EC2 instances.
One of Amazon SageMaker's main features is to provide and manage compute capacity for the multiple stages of machine learning applications. Even though it offers a few serverless options, many scenarios require the use of EC2 instances managed by the SageMaker service.
When choosing an instance type, users must fully understand their workload and evaluate the task's compute and application requirements. For an ML or AI workload, users require instances that provide optimal performance for deep learning and computing. The accelerated computing instance family, which includes p5, g5, trn1 and inf2 instances, can provide teams with this functionality.
Let's discuss why instance types matter, the EC2 instance options for ML workloads and how teams can determine the best instance for their workloads.