Prabhu Ramamoorthy

As enterprises expand their AI capabilities, MLOps platforms are being pushed to their limits—especially with the rise of large language models. LLMOps introduces new complexities, from managing prompt versioning and fine-tuning workflows to optimizing retrieval-augmented generation (RAG) pipelines and ensuring governance at scale.

Financial firms rely upon HPC architectures to develop and backtest trading strategies, calibrate models, and calculate risk. Fast time to market requires fast compute.

GPT-4 and other generative AI could be creating a watershed moment for the software development life cycle. In everything from Python to C++ and even VHDL/FPGA, large language models (LLMs) are starting to help with code generation, reviews, documentation, and testing—including creating data sets to probe the edges of algorithms.