Course Outline
Introduction
Overview of MLOps
- Understanding MLOps
- MLOps within the Azure Machine Learning architecture
Setting Up the MLOps Environment
- Configuring Azure Machine Learning
Ensuring Model Reproducibility
- Utilizing Azure Machine Learning pipelines
- Connecting Machine Learning processes with pipelines
Containers and Deployment
- Packaging models into containers
- Deploying containers
- Validating models
Automating Operations
- Automating operations using Azure Machine Learning and GitHub
- Retraining and testing models
- Rolling out new models
Governance and Control
- Establishing an audit trail
- Managing and monitoring models
Summary and Conclusion
Requirements
- Prior experience with Azure Machine Learning is required.
Audience
- Data Scientists
Testimonials (4)
It was very much what we asked for—and quite a balanced amount of content and exercises that covered the different profiles of the engineers in the company who participated.
Arturo Sanchez - INAIT SA
Course - Microsoft Azure Infrastructure and Deployment
The details and the presentation style.
Cristian Mititean - Accenture Industrial SS
Course - Azure Machine Learning (AML)
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.
