MLOps

MLOps is also from the same family of DevOps, DevSecOps, DataOps, etc. Just like DataOps has a focus on data, MLOps focuses on Machine Learning Model’s end to end journey.

As the name says itself, MLOps (Machine Learning Operations) means from the availability of raw data till the ML model is deployed in PROD, this whole journey is called MLOps.

Please note, CI/ CD is a very critical part of MLOps as in Machine Learning world, 90% models do not go to Production. Most of the models are created, trained, and decommissioned due to N number of reasons. CI/CD plays a vital role of moving models from development to training area again and again and once confirmed by the owner, moved to Prod.

As mentioned, MLOps was extracted from the concept of DevOps, it follows the same pattern but has a different agenda.

The key phases of MLOps are: [These are taken from Best Practice on Databricks, Google and few other places.]

• Model Development
  • Problem Definition
  • Data Selection
  • Data Exploration
  • Data Wrangling
  • Model Preparation
  • Model Validation
• Training and Operationalization
  • Build Model
  • Integration Testing
  • Deployment to Staging
  • Acceptance Testing
  • Deployment to Production
  • Smoke Test
  • Release
• Continues Training
  • Data Ingestion
  • Data Validation
  • Data Transformation
  • Model Training
  • Model Evaluation
  • Model Validation
  • Model Registration
• Model Deployment
  • Build
  • Test
  • Deployment to Production
• Prediction Serving
  • Online Inference
  • Batch Inference
  • Streaming Inference
  • Embedded Inference
  • Explain Predictions
• Continuous Monitoring
  • Data Drift
  • Concept Drift
  • Continuous Drift