Operationalize the data and AI platform and data science model means move the data science models and analytical application into production, managing the models, managing the data platform/data pipes and analytics business applications. The key and important is the machine learning model ops (MLOps) as this is totally a new concept to manage for 24×7 support. But it needs a framework. When AI/ML projects lack a framework and architecture to support model building, deployment, and monitoring – they fail. To succeed, you need collaboration between data scientists, data engineers, business users, and IT operations and app developers for automating and productizing machine-learning algorithms.

DataOps provides a way to operationalize your data platform by extending the concepts of DevOps to the world of data. Extending DevOps, DataOps is built on a simple framework of CI/CD: continuous integration, continuous delivery, and continuous deployment. When you extend this framework further with on an onramp of a data marketplace, you get a solid framework that is MLOps.

The MLOps Framework


There are five steps that form the framework for successful MLOps:

  • Understanding Business Data & KPIs​
  • Data Collection & Preparation
  • ML Model Development
  • ML Model Deployment
  • Monitoring ML Pipeline

Business requirement &  understanding is the first and the most defining step in the process. This means you have clearly defined KPIs (key performance indicators) of the business functions. 

During the data acquisition phase, data scientists and data engineers collaborate on discovering the data needed for machine learning, ingesting and integrating it into the data lake or data warehouse or analytical data hub, ensuring the data quality rules are applied, and data is prepared and ready for ML model building,  validation and deployment 

Model development is the core of the MLOps framework. This is where the data scientist shifts his role from advisory and approver to driver. With the KPIs defined and the datasets ready, the data scientist can leverage his expertise in model development, which is usually iterative until expectations are met. Part of the iterations may involve collaborating with data engineers, business users and data stewards on more data.

With data pipelines established from the data acquisition phase, the data engineer integrates the ML model developed by the data scientist and validates it against the production data. The data pipeline is then deployed into production with DataOps teams for continuous use and monitoring

During the model monitoring phase, a deployed pipeline is integrated with a metrics monitoring mechanism. The DataOps team can then monitor the pipeline metrics, ensuring continued value and increasing confidence in ML.

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