Our development process encompasses various popular machine learning frameworks, ensuring flexibility and adaptability. We have expertise in TensorFlow, PyTorch, scikit-learn, Keras, and MXNet, all offering unique strengths, features, and programming interfaces. By utilizing these frameworks, our developers can select the most suitable one based on their specific needs and project requirements.
To efficiently scale our solutions across multiple GPUs, we leverage PyTorch advanced capabilities, including Distributed Data Parallel (DDP) and Fully Sharded Data Parallel (FSDP) techniques. These techniques enable us to harness the power of parallel processing and achieve optimal performance in distributed training scenarios.
