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.
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.
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.
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.
If you want to systematize your model development process, share your current way of working, and let's determine the right platform configuration together.
You can find answers to frequently asked questions about MLOps Platform in the Knowledge Center.
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