Course Outline
Introduction
MLOps Overview
- What is MLOps?
- MLOps in the Azure Machine Learning architecture for government
Preparing the MLOps Environment
- Setting up Azure Machine Learning for government use
Model Reproducibility
- Working with Azure Machine Learning pipelines to ensure reproducibility
- Integrating Machine Learning processes with pipelines for consistency
Containers and Deployment
- Packaging models into containers for secure deployment
- Deploying containers to production environments
- Validating models to ensure accuracy and reliability
Automating Operations
- Automating operations with Azure Machine Learning and GitHub for streamlined processes
- Retraining and testing models to maintain performance
- Rolling out new models to production environments efficiently
Governance and Control
- Creating an audit trail for transparency and accountability
- Managing and monitoring models to ensure compliance and performance
Summary and Conclusion
Requirements
- Experience with Azure Machine Learning for government applications
Audience
- Data Scientists in the public sector
Testimonials (3)
I've got to try out resources that I've never used before.
Daniel - INIT GmbH
Course - Architecting Microsoft Azure Solutions
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
