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MLOps empowers data scientists and machine learning engineers to bring together their knowledge and skills to simplify the process of going from model development to release/deployment. This allows practitioners to automate the end to end machine Learning lifecycle to frequently update models, test new models, and continuously roll out new ML models alongside your other applications and services. We will be covering how you can get started with MLOps using GH Actions and Azure ML as building blocks.
MLOps empowers data scientists and machine learning engineers to bring together their knowledge and skills to simplify the process of going from model development to release/deployment. This allows practitioners to automate the end to end machine Learning lifecycle to frequently update models, test new models, and continuously roll out new ML models alongside your other applications and services. We will be covering how you can get started with MLOps using GH Actions and Azure ML as building blocks.
8.
Building
a model
Data ingestion Data analysis
Data
transformation
Data validation Data splitting
Model
validation
Training
at scale
Logging
Deploying the
model
Monitoring
Machine Learning Applications Lifecycle