Data & Analytics · AI accelerator
ML Model Deployer
Train + ship + monitor classical + LLM models.
Weeks → days
Model deployment time
3–5×
Models in production
−80%
Silent model failures
5–8 months
Payback period
Ml
ML Model Deployer
DA · No. 88 of 117
What it does
ML Model Deployer in production
- Trains classical and LLM models on versioned data
- Packages models into containers with signed artefacts
- Promotes models through staging with approval gates
- Monitors drift, latency and accuracy in production
- Manages per-store or per-line model fleets
- Rolls back automatically on quality regressions
45-second film: the ML Model Deployer tile, an animated mockup and the KPI impact.
How it ships
Live in 6–9 weeks, inside your perimeter
Deployment options
On-premise (air-gapped)Private cloudManaged cloudHybrid
Integrates with
MLflowDatabricksAmazon SageMakerAzure Machine LearningKubernetesWeights & BiasesGitHub Actions
Technology stack
MLflowKServevLLMEvidently AIPyTorch
Compliance & security
GDPRDPDPSOC 2 Type IIISO 27001
In production
Case studies using ML Model Deployer
Data & Analytics
More Data & Analytics accelerators
Bi
BI Dashboard Builder
Natural-language to dashboard for Power BI, Tableau, Looker.
Dl
Data Lake Architect
Lakehouse design + ingestion bootstrap on Snowflake / Databricks.
Ep
ETL Pipeline Engine
DAG generation, scheduling and lineage tracking.
Rv
Real-Time Visualizer
Sub-second streaming dashboards across ops + finance.
Kp
KPI Monitor
Anomaly detection across business KPIs with narration.
Dq
Data Quality Auditor
Continuous data-contract validation across pipelines.
Ab
A/B Test Analyzer
Reads experiments for significance, guardrail metrics and segment lift, and calls the winner.
Dg
Data Governance Agent
Catalog, lineage, PII and access policy automation.
Cx
Cost Explorer
Cloud + license spend explainability and showback.
See ML Model Deployer running inside your environment
A 20-minute technical walkthrough, no slides. Ships in 6–9 weeks.