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Operationalise Machine Learning With Confidence

Move models from notebook to production fast — and keep them accurate, governed and cost-efficient once they are live.

Shorter time to value
Higher model accuracy in production
Continuous governance & control

Neural-network diagram with data inputs flowing into nodes and outputs

From model experiments to reliable, audit-ready AI platforms.

LESDK’s ML & MLOps practice helps teams industrialise machine learning — standardising the path from data prep and training through deployment, monitoring and retraining. We pair ML engineers and data scientists with a proven MLOps toolchain (feature stores, CI/CD for models, drift detection, policy guardrails) so your predictions stay sharp, explainable and compliant long after go-live.

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Support Features

End-to-end capabilities that turn bespoke models into governed, production-grade services.

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Domain-Expert Collaboration

Data scientists work shoulder-to-shoulder with your SMEs to frame the right problem, surface the right signals and assemble the datasets that actually matter.

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Accelerated Model Training

Distributed GPU training, managed notebooks and pre-built accelerators cut experimentation cycles from weeks to days on both cloud and on-prem.

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Automated Hyperparameter Tuning

Bayesian search, grid search and multi-objective optimisation run as a managed service — surfacing the best candidate models without manual babysitting.

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Deployment & Version Control

Containerised model serving, Git-based versioning and canary rollouts give every model a clear lineage and a safe, reversible path to production.

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Real-Time Performance Monitoring

Live dashboards track latency, accuracy, drift and cost per prediction — flagging issues before they reach the business user.

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Continuous Retraining & Updates

Automated retraining pipelines refresh models on new data and route approvals through governance controls so accuracy holds as the world changes.

Data & Analytics Case Studies

ML and MLOps programs where LESDK moved real enterprise KPIs.

Data-Centre Sunset Planning

Agentic workflows mapped 14,000 legacy workloads to target cloud regions in 9 weeks, cutting plan-cycle time by 60%.

SAP Lift-and-Shift to Cloud

Migration copilot rewrote 2,400 custom ABAP objects and moved a Tier-1 SAP estate to hyperscale cloud with 40% lower run-rate.

Data-Driven Churn Prevention

Transformer-based propensity models flagged at-risk customers 6 weeks earlier and lifted retention 14% on a 9M-account portfolio.

Market Basket Intelligence

Association mining surfaced unseen cross-sell affinities across 180M transactions and boosted average basket size by 8%.

Driver Coaching for Fuel Efficiency

Telematics + NLP coaching hybrid cut fuel burn 12% across a 3,000-vehicle European fleet and paid back in under six months.

Real-Time Vehicle Diagnostics

Sensor fusion + vision models predicted component failures 72h early, shrinking unplanned workshop time by 28%.

Voice-of-Customer Analytics

Sentiment, intent and topic models on 4M support calls cut average handle time 18% and lifted CSAT 11 points in six months.

Intelligent Document Intake

OCR + NLP turned 1.2M scanned supplier invoices into structured data with 98.7% accuracy and 5× faster straight-through processing.

Ready to scale your AI with confidence?

Tell us the model or use case you want to industrialise — forecasting, recommendation, risk scoring, vision inspection — and we’ll shape an ML & MLOps roadmap aligned to your platforms and controls.

Talk to our ML team