Is your organization encountering these critical challenges?

Costly, error-prone AI deployment

Is your manual deployment of AI models, long, prone to error and often leading to costly mistakes being made?

Significant efforts in monitoring your AI models performance

Is the monitoring of your AI models taking away precious time and efforts from your Data Science team?

An ever-increasing number of AI models to operationalize and manage

Are your Data Science and IT teams struggling to operationalize an ever-increasing number of AI models that your organization need?


Meet ML Manager

A turn-key AI management solution empowering organizations in the deployment, operationalization and maintenance of their predictive AI models.

Main Advantages


Full Control

With ML Manager, you get the full view and control of all your predictive AI models with an integrated management tool for your AI operations.

Easy Monitoring

Better monitor your predictive AI production pipeline and model performance using production dashboards, reporting and model diagnostics to identify and address potential data, process and model deficiencies.

Automating

Schedule, orchestrate and automate your predictive AI operation pipelines in order to reduce manual interventions and wasteful task coordination.

Key Benefits


Minimize Time & Effort

Minimize the time and effort spent in deploying predictive AI models, understanding their state, managing and maintaining them in production.

Speed Up Delivery Time

Speed up your delivery time by avoiding conversion of models from one platform to another in order to deploy to production.

Free Up Data Scientists

Free your data science team so that they work on advancing your predictive AI models instead of spending time on AI operationalization and maintenance work.

Using ML Manager

5 steps to AI Operationalization and Management at scale.


Step 1

Connect your existing database and AI servers


Step 2

Register your data, ETL processes and AI models for building your AI pipelines


Step 3

Deploy and serve models as batch processes or APIs


Step 4

Manage your AI Operations


Step 5

Scale your AI portfolio

Want to know how ML Manager can improve your business?