Training data preparation and curation
Sourcing, labeling, and cleaning the data your model needs to learn your domain.
AI Training & Fine-Tuning
Vectrel's AI Training & Fine-Tuning service tests prompting, retrieval, and fine-tuning on your own data before anyone pays to train. If fine-tuning wins, we prepare the data, run it, and compare it against a baseline. If a simpler approach is enough, we tell you.
- Training data preparation & curation
- Base model selection & evaluation
- Fine-tuning execution & iteration
- Performance benchmarking & validation
Overview
General models can struggle with specialized language, custom categories, or strict output formats. This service compares prompting, retrieval, and fine-tuning on data that looks like your real workload before picking an approach. If fine-tuning earns its place, the work can include preparing the data, training, testing, planning the rollout, and setting out when to retrain.
What the work can include.
Base model selection and evaluation
We compare candidate models on your real tasks before picking a starting point.
Fine-tuning execution and iteration
We run the fine-tuning, iterate, and keep a record of what changed and why.
Model performance benchmarking
We measure the model against your data and a baseline, in numbers you can check.
A/B testing against baseline models
A head-to-head test of the new model against the old one under the same conditions.
Deployment and serving infrastructure
The setup that serves the model at the speed, scale, and cost you need.
Ongoing model monitoring and retraining strategy
A plan for watching the model over time and deciding when to retrain it.
Illustrative use cases
Where this can apply.
Real Estate Data Extraction
Illustrative example: compare prompting, retrieval, and fine-tuning for property-detail extraction on a representative evaluation set.
Medical Coding Assistant
Illustrative example: test a coding assistant on the medical terminology you sign off on and send uncertain suggestions to a person for review.
Teams with domain-specific AI needs that general models do not meet on their own.
Technologies
Common options.
PythonPyTorchTensorFlowHugging FaceOpenAI fine-tuning APIAmazon SageMaker AIMicrosoft FoundryVertex AIWeights & BiasesCUDA
FAQ
Frequently asked questions
01
What is AI fine-tuning?
Fine-tuning trains a base model further on data for one specific task. The work can include curating the data, comparing base models, training, testing against a baseline, and planning deployment. We recommend it only when the testing shows it beats prompting, retrieval, or another approach for your workload.
02
Who needs fine-tuning?
It can fit work with domain vocabulary, proprietary categories, or strict output formats where general models fall short: legal language, medical coding, financial documents, industry-specific extraction. The testing decides whether the expected benefit is worth the data and running cost.
03
When is fine-tuning the wrong choice?
We test prompting, few-shot examples, and retrieval first, because they can meet the bar with less data and less complexity. Fine-tuning is a poor fit when there is not enough representative data, the task changes often, or a simpler approach already does the job.
04
How long does a fine-tuning project take?
It depends on data access and quality, any labeling, the model size, how the testing is designed, the training rounds, and the serving needs. The proposal sets out the milestones, what we need from you, the acceptance steps, and the timeline once the workload and the data are assessed.
05
What does a fine-tuning engagement deliver?
Depending on scope, you can get a curated dataset, the training setup, model artifacts, a comparison against a baseline, the test results, a serving design, and criteria for when to retrain. Documentation, artifact access, deployment, and who runs it afterward are set out in the agreement.
Start an AI Training project
Every project starts with a conversation. Tell us what you are working on and we will take it from there.