Fine-tuning & MLOps

AI models trained on your data, deployed and monitored in production

Category: Search and structured data
Model: open, installed on your premises
Data: never leaves
Subscription: €0 per month

01Overview

General-purpose foundation models are powerful but generic. For optimal performance on your specific domain, ticket classification, entity extraction, business content generation, you need a model fine-tuned on your data. Wikolabs handles the full cycle: data preparation, fine-tuning, evaluation, deployment and monitoring.

The problem

Generic models make errors on vocabulary and nuances specific to your sector. Without an MLOps pipeline, models degrade in production undetected. Inference on unoptimized models is expensive. And without deployment infrastructure, models remain POCs that never reach production.

Our answer

We build a complete MLOps pipeline: data preparation and annotation, fine-tuning on your cloud infrastructure (GCP Vertex AI, AWS SageMaker or Azure ML), comparative evaluation against the base model, API endpoint deployment and continuous monitoring (drift, performance, inference cost). The model is automatically retrained when performance degrades.

02How it works

Live in four steps.

01
Data preparation & annotation
Training dataset assembly, annotation (manual or semi-automatic), quality validation and train/eval/test split.
02
Fine-tuning & evaluation
Training on Vertex AI, SageMaker or Azure ML. Business metric evaluation. Base model vs. fine-tuned comparison.
03
Production deployment
Deployment on scalable API endpoint. Inference optimization (quantization, batching). Integration into your existing systems.
04
Monitoring & retraining
Performance metric monitoring, concept drift detection and automatic retraining trigger when needed.

03What you gain

Results you can measure.

Performance +40% vs generic model

A fine-tuned domain model averages 40% better performance on your specific tasks.

Inference cost reduced by 60%

A small specialized model costs much less to run than a large generic model for the same task.

Proprietary model

The model trained on your data belongs to you. It's a strategic asset that grows in value over time.

04Frequently asked

What we get asked before signing.

How much data is needed for fine-tuning?

Between 500 and 5,000 annotated examples for supervised fine-tuning. Techniques like LoRA enable fine-tuning with less data.

Which base models can be fine-tuned?

Proprietary or open models, in every size. Choice depends on your cost, latency and confidentiality constraints.

Can the fine-tuned model run on-premise?

Yes, for open-source models. We manage deployment on your servers or private cloud.

How are model performance measured?

We define business-specific metrics for your use case (precision, recall, F1, BLEU depending on task) and monitor them continuously.

Let's talk about your case for thirty minutes.

A conversation to understand your context, volumes and tools. You leave with a scope and an order of magnitude, no commitment.

+261 38 66 261 00 team@wikolabs.com