Data processing is essential for the functioning of machine learning and is frequently the most complex and expensive workflow to handle.
With the popularity of deep learning and generative AI, the infrastructure needed to support these workloads has become increasingly involved. Tasks such as media transcoding, vector embeddings, computer vision, and NLP all require scaling on top of a heterogeneous cluster or across AI accelerators and CPUs to process data efficiently and quickly.
This whitepaper aims to cover the challenges associated with processing complex or unstructured data for ML. It explores how Ray enables you to scale your existing ML and Python code and optimize any ML data workloads with minimal code changes. Ray schedules jobs across IO, network, CPU, GPU, and xPU with low latency, streams data across the cluster, and maximizes GPUs utilization by leveraging spot instances and auto-scaling across common infrastructure such as Kubernetes and public cloud providers such as AWS and Google Cloud.