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Cloud Services for Machine Learning

Machine learning techniques are used to find valuable underlying patterns within complex data that we would otherwise struggle to discover. The hidden patterns and knowledge about a problem can be used to predict future events and perform all kinds of complex decision making. However, companies building sophisticated machine learning models in-house are likely to run into issues scaling their workloads, because training real-world models typically requires large compute clusters.

Cloud services provide access to GPU (Graphics Processing Units) and TPU (Tensor Processing Units). These processors are capable of executing trillions of floating instructions per second. Due to parallel processor architecture built in GPU and TPU, they are ideal for matrix multiplication operations—the most frequently used operation in machine and deep learning models.

This course will examine popular cloud services and provide a comparison from the perspective of which cloud service is better suited for machine learning models. Next, the course will focus on how to set up cloud servers for machine learning projects. Lastly, machine learning & deep learning (Neural Network) models will be built and run on these cloud servers.

Course Highlights:

  • An introduction and overview of popular cloud platforms (AWS, GCP, Azure, etc.)
  • Developing virtual servers with AWS and GCP
  • Installing machine learning tools – TensorFlow/Keras – on virtual computers
  • A review of ML models (Regression, kNN, Neural Networks, SVM, DT etc.)
  • Ingesting data (images) on virtual systems
  • Run Convolutional Neural Network - ML models on virtual systems
  • Run Generative Adversarial Network (GAN) – ML model on virtual systems
  • Optimize ML models through hyper parameter tuning
  • Analyze results obtained by running ML models on virtual systems

Course Learning Outcomes:

By the end of the course students will be able to:

  • Leverage cloud services for machine learning computing
  • Identify unique computing requirements for machine learning projects
  • Create virtual computing environments for ML projects using popular cloud platforms
  • Install ML software tools (TensorFlow/Keras) on virtual systems
  • Understand various ML models (Regression, kNN, Neural Networks, SVM, DT, etc.)
  • Run ML models on virtual systems

Course Typically Offered: Online during Summer and Winter academic quarters.

Prerequisites: Knowledge of machine learning concepts and the Python programming language.

Next steps: After completing this course, consider taking courses in our specialized certificates in Machine Learning Methods to continue learning.

Contact: For more information about this course, please contact

Note: This course is currently not being offered. 

Course Number: CSE-41331
Credit: 3.00 unit(s)