MLOps (machine learning operations) is the set of practices and tools that take models from experiment to production and keep them working, covering versioning, automated testing and deployment, monitoring, retraining and governance across the whole model lifecycle.
Models degrade as data drifts. Our MLOps services automate the full model lifecycle, from versioned training and CI/CD deployment to drift monitoring and scheduled retraining, so your AI remains accurate, auditable and easy to update.
Maintain, retrain, and deploy AI models reliably at scale. At Prominent AI Services LLP, we deliver MLOps services end to end, from discovery and data preparation to deployment, monitoring and support, so your teams get dependable results quickly and securely.
Maintain, retrain, and deploy AI models reliably at scale.
Ideal for: Enterprises, AI Product Companies. 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 for experiment tracking and model registry, Azure Machine Learning, AWS SageMaker and Databricks for training and deployment, and GitHub Actions or Azure DevOps for CI/CD. Models are containerised with Docker and served on Kubernetes or managed endpoints. Monitoring uses Evidently and cloud-native tools for drift, data quality and performance. Pipelines are written in Python, and infrastructure is defined as code.
Choose a fixed-scope MLOps implementation, or hire DevOps engineers and machine learning developers to run it with your team. If you prefer us to operate models for you, see model as a service and custom AI API hosting.
Struggling to keep models running in production? Tell us about your models and tools, and we will suggest an MLOps roadmap. Start your AI journey.
Common questions about our MLOps & Model Lifecycle Management services.
MLflow, Azure ML, SageMaker, Vertex AI, Kubeflow, GitHub Actions and Azure DevOps, adapted to your platform.
We monitor input data distributions and prediction quality against baselines and alert when thresholds are crossed.
Yes. Pipelines can retrain on new data, evaluate against the current model and promote only when performance improves.
Cost depends on the number of models, your existing tools and the level of automation needed. We usually start with one or two priority models at a fixed price, then roll the same pipeline out to others.
Setting up pipelines and monitoring for the first model typically takes four to eight weeks. Additional models are much faster because they reuse the platform.
Yes, at a lighter level. Even one model in production needs versioning, monitoring and a way to retrain safely. We keep the set-up proportionate to your needs.
Yes. LLM applications need prompt and model versioning, evaluation suites, cost monitoring and safety checks. This is often called LLMOps, and we build it in the same way.
