Accelerate Fine-Tuning Without Sacrificing Control
Ertas gives ML engineers a visual fine-tuning platform for rapid experimentation (Studio) and a secure, on-premise data-preparation pipeline (Data Suite) — so you spend less time on boilerplate and more time on model quality.
The Challenges You Face
Boilerplate Slows Down Experimentation
Setting up training loops, data loaders, adapter configs, and quantization scripts for every experiment is repetitive work that does not improve model quality. Yet skipping any step risks silent regressions or wasted GPU hours.
Data Preparation Is an Untracked Black Box
Cleaning, labeling, and augmenting training data often happens in ad-hoc Jupyter notebooks with no version control or audit trail. When a model regresses, tracing the issue back to a specific data-preparation step is nearly impossible.
GPU Cost Management Is a Full-Time Job
Provisioning the right instance type, managing spot interruptions, and optimizing batch sizes to maximize GPU utilization is infrastructure work that competes with actual research time.
Collaboration Between Data and Training Teams Is Fragile
Data engineers prepare datasets in one environment, ML engineers train in another, and handoffs happen via shared drives or S3 buckets with naming conventions that inevitably break. There is no single source of truth linking a training run to the exact dataset version that produced it.
How Ertas Solves This
Ertas Studio gives you a visual experiment-management layer on top of the fine-tuning workflow you already understand. You still choose your base model, adapter strategy, and hyperparameters — but instead of writing Trainer scripts, you configure them in a GUI that validates settings, launches cloud training jobs, and tracks every run with full reproducibility metadata.
Ertas Data Suite complements Studio by bringing structure to the upstream data pipeline. Running as a native desktop application, Data Suite provides five deterministic modules — Ingest, Clean, Label, Augment, and Export — each producing a versioned, auditable output. Because it runs entirely on-premise, sensitive datasets never leave your network.
Together, the two products give you an end-to-end workflow from raw data to deployed GGUF model with complete lineage tracking, so every production model can be traced back to the exact data preparation steps and training hyperparameters that created it.
Key Features for ML Engineers
Hyperparameter Workspace
Configure LoRA rank, alpha, target modules, learning rate schedules, warmup steps, and evaluation strategies through a structured interface. Every setting is versioned with the run, so reproducing or tweaking a past experiment takes seconds.
Deterministic Data Pipeline
Data Suite's five-module pipeline (Ingest, Clean, Label, Augment, Export) produces identical outputs given identical inputs. Every transformation is logged to an append-only audit trail, making data debugging as rigorous as code debugging.
Run Comparison Dashboard
Overlay loss curves, compare sample outputs, and diff hyperparameter sets across any number of training runs. Filter and sort by metric to quickly identify your best-performing configuration.
Managed Cloud Training
Submit training jobs to managed GPU clusters without provisioning instances. Studio handles driver compatibility, checkpoint saving, and cost-optimized scheduling so you focus on the experiment, not the infrastructure.
Why It Works
- ML engineers using Studio report reducing experiment setup time by over 60%, reallocating that time to dataset curation and hyperparameter exploration.
- Data Suite's audit trail has helped teams pinpoint data-quality regressions that would have taken days to diagnose through manual notebook forensics.
- The GGUF export pipeline supports multiple quantization levels (Q4_K_M, Q5_K_M, Q8_0, F16) so you can balance quality and inference speed for each deployment target.
- Full lineage tracking from raw data through Data Suite to trained model in Studio means every production deployment is reproducible and auditable.
- On-premise Data Suite processing ensures that proprietary or regulated datasets never leave the organization's network, satisfying infosec requirements without slowing down the ML workflow.
Example Workflow
Your team receives a new batch of domain-specific documents that need to become training data for a specialized extraction model. A data engineer opens Ertas Data Suite, ingests the raw PDFs, runs the Clean module to normalize formatting and remove boilerplate, then uses the Label module to tag entity spans with assistance from a pre-trained suggestion model.
Once labeling is complete, the Augment module generates paraphrased variants to increase dataset diversity, and the Export module writes a versioned JSONL file with full provenance metadata. The ML engineer imports that dataset into Ertas Studio, selects a 13B base model, configures a QLoRA adapter with rank 32, and launches a training run. Two hours later, the run comparison dashboard shows a clear improvement over the previous iteration. The winning model is exported as a Q5_K_M GGUF and deployed to the team's inference cluster.
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