Provide an interactive AI development environment on the cloud, enabling AI engineers to write code, experiment, and train models efficiently without managing infrastructure.
Optimize model performance through systematic hyperparameter tuning, helping achieve the highest possible accuracy and efficiency.
Centralize and version trained models and artifacts in one repository, with seamless promotion from training to deployment via pre-built or custom containers, ensuring traceability, governance, and repeatable releases:
Serve trained models as real-time inference services through dynamic, production-ready endpoints and APIs, delivering low-latency predictions for responsive AI-powered applications.
Fine-tune and deploy voice and speech-to-text models to serve customer-facing chatbots, internal copilots, and knowledge assistants with secure access to enterprise data, low-latency inference, and scalable APIs.
Solutions: Notebook instance, GPU instance
Train and deploy RAG chatbots that combine large language models (LLMs) with enterprise knowledge sources to retrieve accurate, context-aware answers from structured and unstructured data without retraining models for every update.
Solution: Network volume, Model-as-a-service, MIG
Train and deploy multimodal AI models to automate enterprise workflows, support intelligent machines, and answer business questions from internal documents. The platform supports training, fine-tuning, and inferencing large Vision-Language Models (VLMs) while efficient management of multi-model
workloads and multi-source data.
Solution: Inference, VAST storage, Model-as-a-service, GPU instance
Train and fine-tune GenAI models for image, voice, and video synthesis, enabling scalable content creation across multiple media formats. This enables organizations to accelerate creative workflows, reduce production costs, and deliver AI-generated content at scale.
Solutions: Notebook instance, multi-node training
