Course Outline
Introduction.
- Machine Learning models versus traditional software.
Overview of the DevOps Workflow.
Overview of the Machine Learning Workflow.
ML as Code Plus Data.
Components of an ML System.
Case Study: A Sales Forecasting Application.
Accessing Data.
Validating Data.
Data Transformation.
From Data Pipeline to ML Pipeline.
Building the Data Model.
Training the Model.
Validating the Model.
Reproducing Model Training.
Deploying a Model.
Serving a Trained Model to Production.
Testing an ML System.
Continuous Delivery Orchestration.
Monitoring the Model.
Data Versioning.
Adapting, Scaling and Maintaining an MLOps Platform.
Troubleshooting.
Summary and Conclusion.
Requirements
- A grasp of the software development cycle.
- Experience in developing or working with Machine Learning models.
- Familiarity with Python programming.
Target Audience
- ML engineers.
- DevOps engineers.
- Data engineers.
- Infrastructure engineers.
- Software developers.
Testimonials (2)
Craig was extremely involved in the training, always making sure we are paying attention, adapted the examples to our day-to-day activities and always provided an answer when asked, even if the information was not added in the presentation.
Ecaterina Ioana Nicoale - BOOKING HOLDINGS ROMANIA SRL
Course - DevOps Foundation®
High level of commitment and knowledge of the trainer
