Course Outline
Introduction
MLOps Overview
- What is MLOps?
- MLOps within the Azure Machine Learning architecture
Preparing the MLOps Environment for Government
- Configuring Azure Machine Learning for government use
Model Reproducibility for Government
- Utilizing Azure Machine Learning pipelines
- Integrating Machine Learning processes with pipelines
Containers and Deployment for Government
- Encapsulating models into containers
- Deploying containerized models
- Validating deployed models
Automating Operations for Government
- Automating operations using Azure Machine Learning and GitHub
- Retraining and testing models in a government context
- Deploying new models to production environments
Governance and Control for Government
- Establishing an audit trail for compliance
- Managing and monitoring models to ensure accountability
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.
