N a n o d e g r e e p r o g r a m s y L l a b u s

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LESSON FIVE

The AzureML SDK

• 

Utilize data with the SDK

• 

Create pipelines

• 

Organize experiments

LESSON SIX

AutoML and 

Hyperparameter

• 

Design solutions with AutoML and the SDK

• 

Analyze model interpretation experiments

• 

Create portable ML models with ONNX 

Course 2: Machine Learning Operations

Operationalizing Machine Learning is a set of best practices that are mostly inherited by the DevOps 

movement. In the past few years, it has become clear that shipping models into production in a reliable, 

reproducible, and automated way with a constant feedback loop is crucial. This is where all the DevOps 

principles come into play and is exactly what this course covers in detail.

Project 2 

Operationalizing 

Machine Learning

MLOps and its core features have been covered in this course in 

detail. This project will apply all the principles from the lessons to 

get a model trained with AutoML and deployed into a production 

environment.

This project covers a lot of the key concepts of operationalizing 

Machine Learning, from selecting the appropriate targets for 

deploying models, to enabling Application Insights, identifying 

problems in logs, and harnessing the power of Azure’s Pipelines. All 

these concepts are part of core DevOps pillars that will allow you to 

demonstrate solid skills for shipping machine learning models into 

production.