MLOps Training is a collection of practices for collaboration and communication between data experts and businesses pros. Applying these practices increases the quality, simplifies the management process, and simplifies the deployment of Machine Learning and Deep Learning models in large-scale development environments. It’s better to align models with business needs, as well as regulating requirements.

MLOps is slowly growing into an impartial method of ML lifecycle management. It is applicable to the complete lifecycle – data collecting, model creation (software development lifecycle, constant integration/continuous delivery), orchestration, deployment, health, analysis, governance, and business metrics.

The key phases of MLOps are:

  • Data gathering

  • Data analysis

  • Data transformation/preparation

  • Model training & development 

  • Model validation 

  • Model serving 

  • Model monitoring 

  • Model re-training.

What is the use of MLOps?

MLOps is a valuable approach for typically the creation and good quality of machine learning and AI solutions. By adopting a MLOps approach, files scientists and equipment learning engineers can easily collaborate and improve the pace of style development and development, by implementing ongoing integration and application (CI/CD) practices having proper monitoring, agreement, and governance involving ML models.