In this self-paced course learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.
After completing this course, students will be able to:
Design and prepare a machine learning solution
Explore data, and run experiments
Train and deploy models
Optimise language models for AI applications
As part of Lumify Group, Lumify Work has skilled more people in Microsoft technologies than any other organisation in Australia and New Zealand. We have a campus in the Philippines, too. We offer the broadest range of instructor-led training courses, from end user to architect level. We are proud to be the winner of the Microsoft MCT Superstars Award for FY24, which formally recognises us as having the highest quality Microsoft Certified Trainers in ANZ.
This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.
Successful Azure Data Scientists start this role with a fundamental knowledge of cloud computing concepts, and experience in general data science and machine learning tools and techniques.
Specifically:
Creating cloud resources in Microsoft Azure
Using Python to explore and visualise data
Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow
If you are completely new to data science and machine learning, please complete Microsoft Azure AI Fundamentals first.
The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.
