Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
コース概要
Introduction
MLOps Overview
- What is MLOps?
- MLOps in Azure Machine Learning architecture
Preparing the MLOps Environment
- Setting up Azure Machine Learning
Model Reproducibility
- Working with Azure Machine Learning pipelines
- Bridging Machine Learning processes with pipelines
Containers and Deployment
- Packaging models into containers
- Deploying containers
- Validating models
Automating Operations
- Automating operations with Azure Machine Learning and GitHub
- Retraining and testing models
- Rolling out new models
Governance and Control
- Creating an audit trail
- Managing and monitoring models
Summary and Conclusion
要求
- Experience with Azure Machine Learning
Audience
- Data Scientists
14 時間
お客様の声 (5)
I've got to try out resources that I've never used before.
Daniel - INIT GmbH
コース - Architecting Microsoft Azure Solutions
とてもフレンドリーで親切
Aktar Hossain - Unit4
コース - Building Microservices with Microsoft Azure Service Fabric (ASF)
Machine Translated
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
コース - MLflow
The practical part, I was able to perform exercises and to test the Microsoft Azure features
Alex Bela - Continental Automotive Romania SRL
コース - Programming for IoT with Azure
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.
