We Build Machine Learning Operationalization (MLOps), we want to provide an end-to-end machine learning development process to design, build and manage reproducible, testable, and evolvable ML-powered software.
The first phase is devoted to business understanding, data understanding and designing the ML-powered software. In this stage, we identify our potential user, design the machine learning solution to solve its problem, and assess the further development of the project.
The objective of an MLOps team is to automate the deployment of ML models into the core software system or as a service component. This means, to automate the complete ML-workflow steps without any manual intervention.
Continuous Integration (CI) extends the testing and validating code and components by adding testing and validating data and models.To understand Model deployment, we first specify the “ML assets” as ML model, its parameters and hyperparameters, training scripts, training and testing data.
Continuous Delivery (CD) concerns with delivery of an ML training pipeline that automatically deploys another the ML model prediction service.
Continuous Training (CT) is unique to ML systems property, which automatically retrains ML models for re-deployment.
Continuous Monitoring (CM) concerns with monitoring production data and model performance metrics, which are bound to business metrics.
Software versions and dependencies should match the production environment, Use a container (Docker) and document its specification, such as image version. Ideally, the same programming language is used for training and deployment.
The goal of the versioning is to treat ML training scrips, ML models and data sets for model training as first-class citizens in DevOps processes by tracking ML models and data sets with version control systems.
The complete development pipeline includes three essential components, data pipeline, ML model pipeline, and application pipeline. In accordance with this separation we distinguish three scopes for testing in for features and data, tests for model development, and tests for ML infrastructure.
Once the ML model has been deployed, it need to be monitored to assure that the ML model performs as expected. Monitor dependency changes throughout the complete pipeline result in notification, Alert if data does not match the schema, which has been specified in the training step.
The ClientoClarify.AI network of partners includes business software providers, niche technology developers, and platform and IT infrastructure vendors.
