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.

With Managed MLOps, you can :

Access the best-in-class resources.

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

Accelerate AI adoption.

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

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

Readiness Assessment

Assess the pre-requisites and readiness of the organization to start with MLOps, outline the gaps, and define the success criteria for MLOps transformation.

Planning

Create a detailed plan for MLOps transformation, clearly document the use case, requirements, technology and tool requirement, and migration timelines.

Data Operations

Algomox helps to identify the right data sources, define the data integrations, apply the data security policies, and ensure data is securely stored in a data lake.

Model Deployment & Serving

Our experts will set up pipelines for your models available in the model registry and use a distributed model serving environment that provides better scalability on demand.

Continuous Monitoring & Optimization

We monitor pipeline usage statistics, data throughput, resource utilization, model drift, and model accuracy and retrain when necessary.

Model Governance & Reporting

We handle MLOps with a strict governance system including audit trails & changelogs. We also provide reporting on performance & service level agreement compliance.