In the realm of automotive software development, the integration of large-scale AI models into autonomous vehicles is becoming increasingly prevalent. The models range from vision AI models to end-to-end AI models for autonomous driving. AI and scientific computing applications are great examples of distributed computing problems. The problems are too large and the computations too intensive to run on a single machine. These computations are broken down into parallel tasks that are distributed across thousands of compute engines, such as CPUs and GPUs. To achieve scalable performance, the system relies on dividing workloads like training data, model parameters, or both, across multiple nodes. These nodes must then frequently exchange information, such as gradients of newly-processed model computations during backpropagation in model training, requiring efficient collective communications like all-reduce, broadcast, and gather and scatter operations. Unlike traditional applications that work with structured data, performance-intensive AI and analytics workloads operate on unstructured data, such as sensor data, audio, images, videos, and other objects. IBM can assist in creating an open hybrid cloud and connected data platform that modernizes infrastructure through global data unification.