Data and ML Operations Consulting

We can help you reduce your cloud and infrastructure bills while improving efficiency in DataOps and MLOps workflows. We can advise you on how to apply rightsizing, auto-scaling, and cost-aware orchestration to minimise storage and compute costs and your optimise cloud spending. For MLOps, similar principles apply - optimised resource allocation and model optimisation techniques combined can noticeably reduce your bill.

To ensure long-term cost control we can help you with implementing FinOps best practices, real-time monitoring, and automation.

We can help you enable seamless, scalable, and automated deployment of data workflows and machine learning models. We design and implement robust CI/CD pipelines tailored to your DataOps and MLOps needs, ensuring rapid, reliable, and efficient model and data pipeline deployments.

By automating data ingestion, transformation, model training, and deployment, we eliminate bottlenecks and streamline production workflows. Our approach integrates best practices in versioning, testing, monitoring, and rollback strategies, ensuring reproducibility and compliance. We leverage containerisation, orchestration, and cloud-native tools to enhance scalability and operational efficiency.

From infrastructure provisioning to real-time monitoring, we build deployment pipelines that accelerate time-to-value while maintaining reliability and governance. Whether modernising existing workflows or setting up pipelines from scratch, we ensure your data and ML models are deployed seamlessly and efficiently.

When you are running hundreds or thousands of pipelines, you don’t want to do it blindly. What you want, is the end-to-end visibility into the health, performance, and reliability of your data workflows and machine learning models so that you can maintain reliability and improve operational efficiency. Our RunReal-time monitoring, logging and alerting detect anomalies, troubleshoot issues, and ensure seamless operations across your DataOps and MLOps estate.

With automated dashboards and intelligent alerts, your teams can gain actionable insights to optimise workflows, automatically track data quality, pipeline efficiency, and model performance, and with integrated key metrics such as drift detection, lineage tracking, and resource utilisation, achieve enhanced compliance, and prevent failures before they impact production.