End-to-end ML training with LoRA/QLoRA fine-tuning, dataset management, model versioning, and cloud GPU provisioning. From data prep to production deployment.
Full-stack ML training infrastructure.
Fine-tune large language models efficiently with Low-Rank Adaptation. Reduce training costs by 90% compared to full fine-tuning.
Organize training datasets by type: person re-ID, face recognition, agent feedback (RLHF), voice prints, object detection, and text classification.
BullMQ-powered job scheduling with progress tracking. Queue multiple training jobs with priority management.
Track model versions, compare performance metrics, and roll back to previous versions with full lineage tracking.
Deploy model versions side by side. Canary deployments with gradual traffic shifting based on performance metrics.
One-click DigitalOcean GPU VM provisioning for training and inference. Automatic start/stop to minimize costs.
Take a model from raw data to a deployed, versioned endpoint without stitching together separate tools. Prepare datasets, launch fine-tuning jobs, review metrics, and promote the winning version, all in one workflow.
Low-Rank Adaptation updates a small set of parameters instead of the full model, cutting training costs by up to 90% compared to full fine-tuning. QLoRA adds quantization so you can adapt larger models on smaller GPUs.
Provision DigitalOcean GPU VMs in one click for both training and inference. Instances start when a job runs and stop automatically when it finishes, so you pay for compute only while it is in use.
LoRA (Low-Rank Adaptation) fine-tunes a small set of added parameters while keeping the base model frozen, which reduces training cost by up to 90% compared to full fine-tuning. QLoRA adds quantization on top, letting you adapt larger models on smaller GPUs with similar quality.
You can organize training datasets by type, including person re-ID, face recognition, agent feedback (RLHF), voice prints, object detection, and text classification. Datasets are managed in one place so you can reuse them across runs.
Jobs run through a BullMQ-powered queue with progress tracking and priority management. You can queue multiple training jobs at once and control the order they run in.
Yes. Every run is versioned with full lineage tracking, so you can compare performance metrics across versions and roll back to a previous version at any time.
GPU VMs are provisioned on DigitalOcean with one click and start and stop automatically around your jobs. Because instances run only while a job is active, you pay for compute only when it is in use.
Fine-tune models on your data. Deploy to production with A/B testing and canary releases.
Thirty minutes, screen shared, using a day that looks like yours. Or start free and have a look yourself.