Course Overview
Machine Learning Operations (MLOps) is based on DevOps principles and practices that increase the efficiency of workflows. For example, continuous integration, delivery, and deployment. MLOps applies these principles to the machine learning process, with the goal of:
- Faster experimentation and development of models
- Faster deployment of models into production
- Quality assurance
Who should attend
- Business Decision Makers
- Compliance Leaders
Prerequisites
None
Course Objectives
One-day workshop, which will employ presentations, case-studies, discussions, and exercises.
Workshop exercises and forum-style discussions will augment and reinforce the courseware to provide a great interactive and collaborative experience for the participants.
Course Content
- Create reproducible ML pipelines. Machine Learning pipelines allow you to define repeatable and reusable steps for your data preparation, training, and scoring processes.
- Create reusable software environments for training and deploying models.
- Register, package, and deploy models from anywhere. You can also track associated metadata required to use the model.
- Capture the governance data for the end-to-end ML lifecycle. The logged information can include who is publishing models, why changes were made, and when models were deployed or used in production.
- Notify and alert on events in the ML lifecycle. For example, experiment completion, model registration, model deployment, and data drift detection.
- Monitor ML applications for operational and ML-related issues. Compare model inputs between training and inference, explore model-specific metrics, and provide monitoring and alerts on your ML infrastructure.
- Automate the end-to-end ML lifecycle with Azure Machine Learning and Azure Pipelines. Using pipelines allows you to frequently update models, test new models, and continuously roll out new ML models alongside your other applications and services.
Instructor
This workshop is delivered by a strongly qualified and certified Microsoft Most Valuable Professional (MVP) with high-profile industry experience.