ML Validated
Data & AI
Machine Learning
Create automated machine learning programs that learn over time, uncover insights about your customers, and predict operational demands.
Overview
We build machine learning solutions on AWS that forecast demand, detect anomalies and personalise customer experiences, using Amazon SageMaker and managed AI services.
Beyond model training, we set up MLOps practices — versioned data and models, automated retraining and monitoring — so models stay accurate in production.
Scope of Work & Technical Methodology
Prescriptive AWS engineering deliverables deployed with Infrastructure as Code (IaC), compliance guardrails, and enterprise SLAs.
End-to-end MLOps lifecycle pipelines with Amazon SageMaker
Predictive models for customer churn, demand forecasting, and fraud prevention
Model monitoring, drift detection, and automated retraining pipelines
Pre-trained computer vision and NLP integration (Amazon Rekognition, Comprehend)
What You Receive
- 1 ML use-case assessment and feasibility study
- 2 Feature engineering and data preparation pipelines
- 3 Model training, tuning and evaluation
- 4 Model deployment with automated MLOps pipelines
- 5 Model monitoring and drift detection
Core AWS Technologies & Tooling
Amazon SageMaker
SageMaker Studio
AWS Glue
Amazon Rekognition
Measurable
Business Impact
Talk to an Engineer
Data-driven predictive intelligence embedded in core operations