--- title: AI Machine Learning | Data & AI Solutions | Claritas One description: AI Machine Learning — production ML, analytics and AI capabilities delivered by the Claritas One data practice. url: https://claritasone.com/solutions/data-ai/ai-machine-learning canonical: https://claritasone.com/solutions/data-ai/ai-machine-learning kind: solution source: https://claritasone.com/solutions/data-ai/ai-machine-learning author: Claritas One datePublished: 2016-01-01 dateModified: 2026-04-18 updated: 2026-04-18 publisher: Claritas One --- # AI & Machine Learning *Solutions / Data & AI* > We design, train, and operationalise custom machine learning systems that generate measurable commercial outcomes — not proof-of-concept models that never reach production. Our ML engineering practice delivers models that are explainable, auditable, and maintained through automated retraining pipelines that keep performance durable over time. [Home](https://claritasone.com/) › [Solutions](https://claritasone.com/solutions) › [Data & AI Solutions](https://claritasone.com/solutions/data-ai) › **AI Machine Learning** ## Overview Most enterprise AI initiatives fail not because the models are technically inadequate, but because the organisation has not built the infrastructure to operationalise them reliably. A model that achieves 94% validation accuracy in a notebook environment can degrade to 71% accuracy in six months if feature drift goes undetected. Our ML engineering practice is designed around production durability: we instrument every model with drift detection, performance benchmarks, and automated retraining triggers so that the business value delivered at launch compounds rather than erodes. Organisations that engage us leave with a model, an MLOps platform, and an internal team that understands how to maintain it. ## Our Approach ### 1. Problem Framing & Commercial Value Quantification We work with your business and data leadership to translate commercial objectives into precise ML problem statements — framing the prediction target, acceptable error tolerance, and minimum performance threshold required to justify deployment. Every engagement begins with a signed-off business case before a single model is trained. ### 2. Data Architecture & Feature Engineering Our data engineers assess your existing data infrastructure, identify quality gaps, and build automated feature engineering pipelines that transform raw data into a model-ready feature store. Feature lineage is documented to satisfy explainability requirements in regulated industries. ### 3. Model Development & Validation We train and evaluate model candidates using rigorous hold-out validation, cross-validation, and adversarial testing strategies. Model selection decisions are documented with performance metrics, fairness assessments, and out-of-distribution behaviour analysis — supporting regulatory submissions where required. ### 4. MLOps Platform & Production Deployment We deploy models to production through standardised serving infrastructure — real-time inference APIs, batch prediction pipelines, or embedded model endpoints — with automated rollback, canary deployment, and A/B testing built into the release process. ### 5. Monitoring, Drift Detection & Retraining Automation Every production model is instrumented with data drift monitors, prediction distribution tracking, and business metric correlations. Automated retraining pipelines trigger when performance degrades below defined SLAs — ensuring model ROI is sustained without manual intervention. ## Capabilities - Custom model development: classification, regression, anomaly detection, and ranking - Deep learning engineering with PyTorch, TensorFlow, and JAX - Feature store implementation with Feast, Tecton, or custom solutions - MLOps platform engineering with MLflow, Kubeflow, and SageMaker Pipelines - Real-time inference API deployment with sub-100ms latency SLA - Automated drift detection and model performance monitoring - Explainable AI (XAI) with SHAP, LIME, and regulatory reporting - Fairness assessment and bias mitigation frameworks ## Outcomes | Metric | Value | | --- | --- | | Average model accuracy in production deployments | **94%** | | Real-time inference latency SLA | **< 100ms** | | Faster path from prototype to production vs. in-house | **3×** | | Average model performance durability with our MLOps layer | **18 months** | ## Next Step **Deploy AI That Generates Durable Commercial Returns** Our ML engineering leadership will assess your data assets and commercial objectives to determine the highest-ROI AI use cases for your organisation. → [Get a proposal](https://claritasone.com/get-a-proposal) · [Contact us](https://claritasone.com/contact) --- View the live page: About Claritas One: · Contact: · All pages: