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MLOPS

MLOPS

How We Help With Your MLOPS

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.

It’s time to turn the odds in your favor. With 360 Tech Hub — a machine learning development platform, set up and managed by us, ready for a spin from you.

Data Preparation & Management

  • Program offline extraction or batch fetching from the target data source.
  • Automate data validation against a set schema to cleanse it.
  • uto-distribute validated data into training/validation data sets.
  • Create a feature store — a catalog for organizing pre-made features

Model Training

  • 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.

Model Evaluation

  • 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.

Model Serving

  • 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.

Model Monitoring

  • 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.

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