Course Outline

Introduction

Overview of Azure Machine Learning (AML) Features and Architecture

Overview of an End-to-End Workflow in AML (Azure Machine Learning Pipelines)

Provisioning Virtual Machines in the Cloud

Scaling Considerations (CPUs, GPUs, and FPGAs)

Navigating Azure Machine Learning Studio

Preparing Data

Building a Model

Training and Testing a Model

Registering a Trained Model

Building a Model Image

Deploying a Model

Monitoring a Model in Production

Troubleshooting

Summary and Conclusion

Requirements

  • An understanding of machine learning concepts.
  • Knowledge of cloud computing concepts.
  • A general understanding of containers (Docker) and orchestration (Kubernetes).
  • Python or R programming experience is helpful.
  • Experience working with a command line.

Audience

  • Data science engineers
  • DevOps engineers interested in machine learning model deployment
  • Infrastructure engineers interesting in machine learning model deployment
  • Software engineers wishing to automate the integration and deployment of machine learning features with their application
  21 Hours
 

Testimonials

Related Courses

AdaBoost Python for Machine Learning

  14 hours

Artificial Intelligence (AI) with H2O

  14 hours

AutoML with Auto-Keras

  14 hours

AutoML

  14 hours

Google Cloud AutoML

  7 hours

AutoML with Auto-sklearn

  14 hours

Building Microservices with Microsoft Azure Service Fabric (ASF)

  21 hours

Pattern Recognition

  21 hours

DataRobot

  7 hours

Data Mining with Weka

  14 hours

H2O AutoML

  14 hours

Machine Learning for Mobile Apps using Google’s ML Kit

  14 hours

Pattern Matching

  14 hours

Machine Learning with Random Forest

  14 hours

RapidMiner for Machine Learning and Predictive Analytics

  14 hours