Machine learning and AI engineering
Model development, computer vision and evaluation with an MLOps path to production.
Conceptual visualization only
Overview
From a framed problem and an honest baseline to an evaluated model with monitoring. We prioritise measurable evaluation over model novelty: data splits, metrics that match the decision, error analysis and a deployment path your team can maintain.
Problems it solves
- • Models with impressive demos and no evaluation protocol
- • Data leakage and unrealistic validation splits
- • No monitoring after deployment, so drift goes unnoticed
- • Computer-vision pipelines that fail on real field imagery
Who it suits
- • Data science teams
- • Product teams adding prediction features
- • Operations teams automating inspection
What this capability does
- • Problem framing and baseline modelling
- • Tabular, time-series and vision model development
- • Evaluation design, error analysis and reporting
- • Model packaging and inference services
- • Monitoring, retraining and drift checks
How This AI Agent Works
The delivery process for machine learning and ai engineering: trigger, validation, approved tooling, human approval and the deliverable produced.
AI engineering workflow
From a requirement to a deployed, monitored model.
Saved with the run so the recorded execution trail shows what was asked. Demonstration runs use sample data. They do not touch your systems.
Public visitors can preview the structure and inspect every node. Labelled demonstrations and live runs require a signed-in account with configured providers.
Workflow summary as a text list
- 1. Requirement: Trigger. Business objective, target metric and constraints.
- 2. Dataset and model assessment: Validation. Check data sufficiency, leakage and licence terms.
- 3. Experiment: Approved tool. Train candidate models and log every run.
- 4. Evaluation: AI agent. Compare candidates on the agreed metrics and slices.
- 5. Security check: Validation. Review data handling, secrets and model exposure.
- 6. Deployment approval: Human approval. An owner approves before the model is promoted. Human approval required.
- 7. Model deployment: Action. Ship the approved model behind a versioned endpoint.
- 8. Monitoring: Monitoring. Track drift, latency, cost and error rates.
- 9. Model package: Output. Model card, evaluation report and deployment record.
Approval gate: Deployment approval. Deliverable: Report.
Workflow audit for this page
| Template ID | Nodes | Approval node | Output type | Preview | Demo | Live | Mobile test |
|---|---|---|---|---|---|---|---|
| machine-learning-ai-engineering | 9 | Yes. Policy: always | Report | Available | Available (signed in) | Available via Studio · requires ai_provider | Not yet covered |
Demonstration runs are stored and replayed from recorded events. Live runs execute server-side in the Studio and only start when the listed integrations report healthy.
Supported industries
- Manufacturing
- Retail and e-commerce
- Healthcare
- Agriculture
- Insurance
Technology stack
Every supported technology for this capability.
- scikit-learn
- PyTorch
- TensorFlow
- Keras
- XGBoost
- OpenCV
- Transformers
- Hugging Face
- Evaluation
- MLOps
Specialist AI agents
ML engineering agent
Modelling approach and experiment plan
Profile not published — this role runs inside the capability workflow.
Computer vision agent
Vision pipeline design
Profile not published — this role runs inside the capability workflow.
Model evaluation agent
Metrics, splits and error analysis
Profile not published — this role runs inside the capability workflow.
MLOps agent
Deployment and monitoring plan
Profile not published — this role runs inside the capability workflow.
Workflow
Every run follows the same auditable spine.
- Step 1
Request
You describe the objective, constraints and delivery format.
- Step 2
Validate inputs
Files, schemas, credentials and scope are checked before any agent starts.
- Step 3
Assign agent
The registry resolves the specialist agent and its permitted tools server-side.
- Step 4
Use approved tools
Only tools allowed for that agent and your plan can execute.
- Step 5
Process data
The agent frames the problem, proposes baselines, metrics and an experiment schedule.
- Step 6
Validate output
Evaluation design is checked for leakage, split integrity and metric fit.
- Step 7
Human review if required
Sensitive outputs are withheld until an approver releases them.
- Step 8
Deliver result
Approved deliverables are released to your workspace with an export trail.
Inputs and outputs
What you provide
- • Dataset sample with a data dictionary
- • Label definitions and quality notes
- • Target metric and decision threshold
- • Latency and deployment constraints
What you receive
- • Assessment and feasibility note
- • Baseline and experiment plan
- • Evaluation protocol with metrics
- • Deployment and monitoring outline
Use cases
- Defect detection on production-line imagery
- Demand forecasting with confidence intervals
- Document classification and extraction at scale
- Crop-type classification from satellite time series
Integration status
Real configuration states recorded by health checks — nothing here is assumed to be connected.
- AI providerSign in to view status
The model gateway every agent run and consultant reply depends on.
- File storageSign in to view status
Requirement-file uploads and generated artifact storage.
- Spatial processor (hosted Python service)Sign in to view status
Your FastAPI service: GeoPandas, GDAL, Rasterio and IfcOpenShell work runs there, not in this app.