Machine Learning Operations (MLOps) solves problems, unique to every aspect of the machine learning model lifecycle by melding tested DevOps approaches with data management best practices into a repeatable framework for model development, testing, and deployment
How we can help you with MlOps
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Data Preparation & Management
- Program offline extraction or batch fetching from the target data source.
- Automate data validation against a set schema to cleanse it.
- Auto-distribute validated data into training/validation data sets.
- Create a feature store — a catalog for organizing pre-made features
- Select a lineup of storage agnostic version control systems, adapted for ML workflows.
- Integrate them into the platform and configure them.
- Check that metadata from new training runs gets auto-committed to version control.
- Build a metadata store to capture t relevant information for further analysis.
- Set up a framework for model monitoring and validation, using the selected toolkit
- Ensure auto-capture of all the essential performance data from each model run
- Record and store all the tidbits for easy reproducibility.
- Create specific triggers for launching pre-training when the model didn’t perform well.
- Decide on the optimal framework for wrapping the model as an API service.
- Or select and configure a container service for deployment.
- Create a production-ready repository of models
- And set up a model registry where all the relevant model metadata is stored.
- Pick the optimal agent for real-time model monitoring.
- Configure it to capture anomalies, detect concept drift, and monitor model accuracy.
- Add extra measures for estimating model resource consumption.
- Specify re-training triggers and configure alerts.