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.
Get access to the the latest AI technology and experienced customer-focused experts with our packaged solution.
Innovate faster and create effective long-lasting AI solutions by speeding up the AI transformation cycle with Managed MLOps.
MLOps platforms are costly to build and resource-intensive to maintain. By outsourcing this, you can focus on domain problems and higher priority innovations.
Assess the pre-requisites and readiness of the organization to start with MLOps, outline the gaps, and define the success criteria for MLOps transformation.
Create a detailed plan for MLOps transformation, clearly document the use case, requirements, technology and tool requirement, and migration timelines.
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.
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.
We monitor pipeline usage statistics, data throughput, resource utilization, model drift, and model accuracy and retrain when necessary.
We handle MLOps with a strict governance system including audit trails & changelogs. We also provide reporting on performance & service level agreement compliance.