MLOps: From Model Development to Deployment
Training Provider: NATIONAL UNIVERSITY OF SINGAPORE
Course Reference: TGS-2026064813
S$1,575
Original: S$5,250
Save S$3,675
About This Course
The course is to enable participants to architect and operationalise production-ready Artificial Intelligence/Machine Learning (AI/ML) solutions through well-defined Machine
Learning Operations (MLOps) workflows. Leveraging advanced knowledge of statistical modelling and core ML architecture, participants will master the skills required to prepare
technical blueprints, apply different Machine learning algorithms, and build robust operational and data pipelines to ensure the developed solutions are scalable, reliable, and aligned with
defined business and performance metrics throughout the full machine learning lifecycle.
What You'll Learn
At the end of the course, participants will be able to:
Data Processing Pipelines: Build and develop robust machine learning data pipelines and manage the computing infrastructure for data processing and model training
Architecture and Development: Define, structure, and architect complete AI/ML solutions and MLOps workflows that ensure the solutions meet all specified metrics throughout the full machine learning lifecycle.
Algorithm Validation: Evaluate and apply machine learning models and algorithms, developing test procedures to evaluate and validate model performance in the intended operating environment.
Solution Deployment: Develop and implement the final solution, ensuring the use of appropriate techniques and cloud-based architectures to meet the contextualized usage scenarios and business requirements.
Responsible MLOps and Security: Define, design, and implement an end-to-end MLOps workflow incorporating robust software engineering principles, continuous model monitoring and feedback loops, fairness, bias, and transparency in pipelines, and security best practices, ensuring the deployed AI/ML solution is reliable, scalable, and resilient throughout the entire operational lifecycle.
Data Processing Pipelines: Build and develop robust machine learning data pipelines and manage the computing infrastructure for data processing and model training
Architecture and Development: Define, structure, and architect complete AI/ML solutions and MLOps workflows that ensure the solutions meet all specified metrics throughout the full machine learning lifecycle.
Algorithm Validation: Evaluate and apply machine learning models and algorithms, developing test procedures to evaluate and validate model performance in the intended operating environment.
Solution Deployment: Develop and implement the final solution, ensuring the use of appropriate techniques and cloud-based architectures to meet the contextualized usage scenarios and business requirements.
Responsible MLOps and Security: Define, design, and implement an end-to-end MLOps workflow incorporating robust software engineering principles, continuous model monitoring and feedback loops, fairness, bias, and transparency in pipelines, and security best practices, ensuring the deployed AI/ML solution is reliable, scalable, and resilient throughout the entire operational lifecycle.
Entry Requirements
Minimum Diploma and knowledge in Python
Course Details
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