Full ML lifecycle: data preparation, training, fine-tuning, evaluation, and deployment with multi-tier GPU acceleration from T4 through H100.
Problem
Teams waste weeks configuring environments, fighting CUDA versions, and managing GPU scheduling. Training jobs fail silently. Model deployment requires entirely different tooling from training. And reproducing results from three months ago is nearly impossible.
ML Lifecycle
Pre-configured PyTorch, TensorFlow, JAX, and HuggingFace environments with GPU auto-provisioning.
Dataset versioning with DVC, automated labelling pipelines, augmentation strategies, and data quality validation with lineage tracking.
Multi-GPU (up to 8x H100) and multi-node training with DeepSpeed, FSDP, and automatic mixed precision. Auto-scaling based on job requirements.
LoRA, QLoRA, and full fine-tuning for Llama, Mistral, Qwen, and custom architectures. Quantization (GPTQ, AWQ) for deployment optimization.
Hyperparameter logging, training curves, metric comparison across runs, and artifact versioning. Built-in W&B and MLflow integration.
One-click deployment to inference endpoints with TensorRT optimization, auto-scaling, and A/B testing. vLLM for LLM serving.
Version management, model cards, approval workflows, and lineage from training data through to production predictions.
GPU Tiers
Inference, small model training, prototyping. Cost-effective for development cycles.
Medium model training, fine-tuning 7B-13B models, batch inference workloads.
Large model training, distributed workloads, 70B model fine-tuning with FSDP.
Frontier model training, maximum throughput. NVLink multi-GPU for 100B+ parameters.
Use Cases
Custom classification, NER, and prediction models trained on proprietary data.
Satellite imagery segmentation, change detection, and spatial prediction.
Defect detection, predictive maintenance, and quality optimization models.
Drug discovery, genomics, and medical imaging model development.
From intent to verified engineering artifact.