Discipline 05

Data, MLOps & Model Training

WeLead Lab builds the data and MLOps foundation under production AI — pipelines, feature stores, model deployment, monitoring, and fine-tuning (SFT, RLHF, DPO) — so models stay accurate and reliable over time.

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AI fails in production when the plumbing is missing. We build the data engineering and MLOps layer — pipelines, feature stores, experiment tracking, model registry, serving, and monitoring — plus model training and fine-tuning, so your AI is reproducible, observable, and dependable.

Who it's for

Teams whose models work in a notebook but not in production
Orgs that need reproducible, observable ML operations
Companies fine-tuning or training their own models

What you get

Reliable data pipelines and a feature store
Deployment, monitoring, and drift detection in place
A reproducible training and fine-tuning workflow

What's included

18 services in this discipline

Data engineering & ETL pipelines

Batch & streaming pipelines

Change data capture (CDC)

Data quality & validation

Feature stores & feature platforms

Vector databases

Data versioning & lineage

Experiment tracking

Model registry & versioning

Workflow orchestration (DAGs)

Model serving & deployment

Model monitoring & drift detection

Observability & tracing

MLOps consulting & CI/CD

Cloud & lakehouse infrastructure

Platform migration

Fine-tuning (SFT, RLHF, DPO)

Human data generation & annotation

How we engage

No mystery, no lock-in before you see value.

Scan

We map your business, stack, and data and surface the highest-ROI opportunities in this discipline. Free, no obligation.

Audit

A prioritized plan: what to build, buy, or skip — sequenced by ROI and risk. Yours to keep either way.

Build · Run · Govern

We ship the systems, run them in production, and govern them for safety, cost, and compliance.

Data, MLOps & Model Training — FAQ

What is MLOps and why do we need it?

MLOps is the practice and tooling that makes machine learning reliable in production — experiment tracking, model registry, deployment, monitoring, and drift detection. Without it, models silently degrade and nobody can reproduce or roll back. We build that foundation so your AI stays dependable.

Do you do data engineering too?

Yes. Good AI starts with good data, so we build the pipelines (batch and streaming), data quality checks, change data capture, and feature stores that feed your models — on your existing cloud or lakehouse.

Can you fine-tune or train custom models?

Yes. We run supervised fine-tuning (SFT), RLHF, and DPO, including human data generation and annotation, with a reproducible, evaluated workflow — so a fine-tune is a measured improvement, not a gamble.

Explore the other disciplines

One partner for the whole of AI — 110 services across 8 disciplines.

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A plan you can defend to your board and your regulator.

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Software that does the work, not just shows it.

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Custom models for the problems off-the-shelf can't solve.

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Get found and chosen — on Google and on every AI.

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Production-grade output at machine speed.

Industry Solutions

AI built for how your sector actually works.

Ready to put AI to work?

A 20-minute scan — no pitch, just the highest-ROI plan for your business.

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Austin, TX · [email protected] · (512) 336-9618