Data science integration services put analytics and machine learning to work inside your live operations, connecting data sources and APIs, running models in production with MLOps, and monitoring data streams in real time so insights reach people and systems when they are needed.
Insights only create value when they reach the right systems at the right time. Our data science integration services connect analytics and machine learning to live data streams, external APIs and production applications, so decisions happen automatically and continuously.
We also put the operational backbone in place, with MLOps practices that version, deploy, monitor and retrain models safely. This keeps your AI accurate as markets, customers and data change.
Services that keep data and models connected, current and production-ready.
We assess your current data flows and model operations, design an integration and MLOps architecture, automate deployment pipelines and set up monitoring dashboards and alerts for data and model health.
We use MLflow, Azure Machine Learning, AWS SageMaker and Databricks for model operations; Kafka, Event Hubs and Kinesis for streaming; and REST APIs built in ASP.NET Core, Node.js and Python. Data platforms run on Microsoft Fabric and cloud warehouses, with CI/CD through GitHub Actions or Azure DevOps.
Choose a fixed-scope project, managed operations, or hire data engineers and DevOps engineers to work with your team. To build the underlying models and data platform, see data science development services.
Need your data and models working in real time? Tell us about your systems, and we will propose an integration approach. Start your AI journey.
Answers to common questions about our Data Science Integration Services.
MLOps applies DevOps practices to machine learning, automating testing, deployment and monitoring so models stay reliable after launch.
Yes. We ingest and normalise data from partner APIs, marketplaces, payment gateways and SaaS tools into your analytics platform.
Yes. We offer managed support for pipelines and models, including monitoring, incident response and scheduled retraining.
Cost depends on the number of systems, data volumes and real-time requirements. We usually start with one priority integration or model at a fixed price.
Simple integrations take a few weeks. Real-time platforms and MLOps for several models are delivered in phases over two to four months.
Yes. We extend existing platforms on Azure, AWS, Databricks or on-premise databases rather than replacing them, unless there is a clear benefit in doing so.
You can choose a managed support plan with us, or we hand over documentation and runbooks and train your team to run them.
