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MLops Platform

vMind MLOps Platform manages the model development process from a single place, from experiment to production. Experiment tracking, a model repository, version comparison and a deployment pipeline work on the same platform; which data produced which model and which version is in production can be tracked at any time. It runs on Kubernetes-based infrastructure and is directly integrated with vMind GPU resources.

What Is MLOps?

MLOps is the systematization of the development, testing, deployment and monitoring processes of machine learning models. It is the adaptation of continuous integration and deployment practices from software development to model development.

Without MLOps, models stay on developers' computers and in notebooks. Which version is in production, which data it was trained on and how its performance changes over time cannot be tracked. 

How Does the MLOps Platform Work?

The parameters, metrics and outputs of every training run are recorded automatically. Trained models are versioned, stored in the model repository and tagged. Which dataset produced which model is recorded.

At the production stage, the model is rolled out gradually; if a performance problem is seen, it returns to the previous version. The metric drift of the model in production is monitored, and the need for retraining is detected early. 

Why vMind MLOps Platform: Advantages

Don't Let Your Model Stay in the Lab 


Most artificial intelligence projects fail to produce results not because of technical failure, but because they cannot be moved to production. 

Experiment Tracking Experiment Tracking
Versioned Model Repository Versioned Model Repository
Data Lineage Data Lineage
Gradual Deployment Gradual Deployment
GPU Integration GPU Integration

What Do We Offer Under the MLOps Platform?

  • 1- Experiment tracking...
    Keeping records of the parameters, metrics and outputs of training runs.
  • 2- Model repository...
    Versioned model storage and tagging infrastructure.
  • 3- Data lineage...
    Tracking which dataset produced which model.
  • 4- Deployment pipeline...
    Rolling the model out gradually to test and production environments.
  • 5- Performance monitoring...
    Detecting metric drift of the model in production.
  • 6- GPU integration...
    Running training jobs on the vMind GPU cluster.

If you want to systematize your model development process, share your current way of working, and let's determine the right platform configuration together.

Knowledge Center

You can find answers to frequently asked questions about MLOps Platform in the Knowledge Center.

Click here to go to the Knowledge Center. 

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