MLOps & DataOps

As part of our MLOps & DataOps service, we develop and deploy automated pipelines for data processing, model training and model deployment. We set up tools for proactive monitoring of data jobs, ML models, analysing data drifts, and analysing predictive accuracy.

Operations Support

  • Deployment pipelines are automated
  • Data pipelines are neatly integrated for filtering, masking and cleansing
  • Data is prepared periodically for a new training round
  • Manage configurations, resources, and provisioning for training and production deployment
  • Setting up tracking and versioning for experiments and model training runs
  • Setting up the deployment and monitoring pipelines for the models that do get to production

Application Support

  • Incident management and response
  • Assisting end-users in using the application and answering their queries
  • Monitoring and alerting for problems and performance issues
  • Deploying centralized logging
  • Troubleshooting for deployment, capacity, connectivity, resources, and function
  • Management of applications & platform configurations
  • Coordination of changes and service requests
  • Feedback to development teams for application bugs and glitches
  • Application documentation