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

How we can help you with MlOps

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.
  • Auto-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.