Manage and monitor your AI and machine learning pipelines.

Any data scientist can make a model. But managing a model life cycle takes expertise and a powerful platform. Our ModelOps engineers manage a model in production by monitoring model drift, tracking model accuracy, and handling retraining and hot-swaps.

AI Application

With Managed MLOps, you can :

Combine the power of MLOps platform and skilled data scientists

Access the best-in-class resources.

Get access to the the latest AI technology and experienced customer-focused experts with our packaged solution.

Speed up innovation by outsourcing model management

Accelerate AI adoption.

Innovate faster and create effective long-lasting AI solutions by speeding up the AI transformation cycle with Managed MLOps.

Save on research and development costs

Reduce your AI cost of ownership.

MLOps platforms are costly to build and resource-intensive to maintain. By outsourcing this, you can focus on domain problems and higher priority innovations.

How can we help you?

Create, Operate, and Manage your AI solutions using MLOps methodologies
AI model management
Machine learning models are frequently replaced with better performing ones. Pipelines, on the other hand, are stable and used with different versions of the model. We handle model registry, versioning, and governance with features like hot-swap to switch model versions on-the- go without loss of data.

AI pipeline management & DataOps
Data can come in from many sources. It can be internal information across many databases or it could be external information that is used for model prediction. We manage it all with pipeline creation, serving & maintenance. We incorporate DataOps concepts to securely manage data operations.

AI application support & maintenance
Every software component needs to be changed and improved continuously. Defect fixes, configuration updates, and feature enhancements are must for the continuous performance of the AI application.

Continuous monitoring
It is a fatal error to think models performance is constant. Model performance is affected by data drift and model accuracy can degrade over time naturally due to changing patterns of external behaviour. We track model accuracy, monitor concept and data drift so that model results do not affect business operations negatively.

Continuous optimization
We are constantly tracking all pipeline and model performance outside of accuracy too. We look at computational optimization, resource utilization and better alignment with business KPIs to define retraining and improvement design.

Service Methodology

Managed MLOps-We handle DataOps, ModelOps, and Model Retraining and keep you up to date with all your model performance.

Target Operating Model

We bring a strong MLOps platform managed by our AI experts that incorporate the best practices in data science for your enterprise data. Get in touch with us and we can demonstrate how we apply Managed MLOps to meet your AI goals.
Managed MLOps Target Operating Model-Combining the elements of MLOps you get strong governance & best practices of the industry.
Have any questions?

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