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Machine Learning Operations

The University of Sydney
Sydney , Australia
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About Program

Program Overview

Machine Learning Operations

Master practical skills to deploy and manage AI and machine learning systems.


Overview

This microcredential is designed to equip learners with a suite of skills in the operationalisation of machine Learning (ML) and Artificial Intelligence (AI) tools.


The curriculum emphasises practical applications and problem-solving in real-world scenarios. Learners will quickly build a portfolio to demonstrate the Machine Learning (ML)/Artificial Intelligence (AI) skills they have acquired.


What you'll learn

By the end of this course, you will be able to:

  • Demonstrate fundamental and specialised skills in the operationalisation of ML and AI tools.
  • Use industry-standard ML/AI tools and Continuous Integration/Continuous Deployment (CI/CD) platforms.
  • Develop systems that use ML/AI tools to tackle complex analytical challenges.

School of Computer Science

This course is delivered in partnership with the School of Computer Science. The School of Computer Science aims to provide research and education excellence in all areas related to computer science and technological innovation.


Aims

Machine Learning Operations will bridge the gap between theoretical knowledge and practical application in the field of Machine Learning (ML) and Artificial Intelligence (AI). The aim of this microcredential is to equip learners with a deeper understanding of MLOps through hands-on practice and project work, ensuring that participants are well-prepared to develop, deploy, and maintain ML and AI applications to address complex real-world problems.


Content

Foundations of Machine Learning and MLOps

  • Introduction to Machine Learning (ML) concepts.
  • Data preparation and pre-processing techniques.
  • Model development and evaluation.
  • Introduction to MLOps and its significance.
  • Setting up the MLOps environment: Tools and platforms.

MLOps Tools and Version Control (I)

  • Deep dive into ML/AI tools (TensorFlow, PyTorch).
  • Version control systems (Git) for ML projects.
  • Hands-on project: Implementing ML/AI and manage using VCS.

MLOps Tools and Version Control (II)

  • Containerization with Docker and Kubernetes in ML.
  • Continuous Integration and Continuous Deployment (CI/CD) in ML.
  • Hands-on project: Implementing CI/CD in an ML project.

Advanced MLOps Techniques (I)

  • Advanced model training techniques.
  • Hyperparameter tuning and optimization.
  • Project work: Building and training an ML model.

Advanced MLOps Techniques (II)

  • Model serving and deployment strategies.
  • Monitoring and logging for ML models in production.
  • Project work: Deploying and monitoring an ML model.

Real-World Applications and Capstone Project

  • Case studies: Successful MLOps implementations.
  • Industry speakers and Q&A sessions.
  • Capstone project ideation and planning.
  • Capstone project development.
  • Capstone project presentations and feedback.

Who is this course for?

  • Professionals: including Developers and Technical Engineers with expertise in software engineering, data science, or IT operations who are seeking to specialise in MLOps.
  • Senior ML Ops leaders running ML Ops teams looking to provide standardised practical training that meets industry needs and enables companies to streamline their ML workflows.
  • Entrepreneurs: Start-up owners interested in efficiently deploying ML models.
  • Graduates from computer science or related fields seeking practical, industry-relevant skills.

Course delivery style

The focus will be on collaboration among students and between teachers and students where they will be asked to connect and respond to content to maintain momentum and ensure a continued connection.


The format features live sessions, pre-recorded lectures, videos, and interactive online activities, providing a comprehensive and engaging educational experience. Learners will participate in hands-on practice and project work, ensuring that they are well-prepared to implement MLOps in real-world scenarios.


Prerequisites

Learners will need to have an understanding of machine learning and artificial intelligence, with an advanced level of programming skills. Proficiency in Python is desirable.


Course assessments

The final week’s capstone project would serve as a culmination of the skills learned throughout the course.


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