Overview
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| Duration |
3 days
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| Enquiry |
Please contact ask-iss@nus.edu.sg for more details.
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Organisations can build machine learning models, but struggle to turn them into reliable business solutions. Models often remain in notebooks, pilots or isolated prototypes because teams lack the deployment, monitoring, governance and operating practices needed for production AI. Without these capabilities, AI initiatives can be slow to scale, difficult to maintain, and hard for business users to trust.
Data Science Deployment is a 3-day intermediate course that helps you move machine learning and AI models from development into production. As organizations scale their use of machine learning and AI, they face increasing challenges in operationalizing models through CI/CD pipelines, APIs, and cloud-based infrastructure. These challenges require strong MLOps practices to ensure that models can be reliably deployed, updated, and maintained in production environments.
The course focuses on the practical work needed after a model is built: choosing deployment strategies, supporting batch and real-time use cases, managing model serving infrastructure, applying agile delivery practices, and using automation to improve reliability and speed. You will also explore how AI-assisted development tools can help accelerate coding, testing, documentation, troubleshooting and deployment workflows, while still applying human review, governance and responsible AI controls.
By the end of the course, you will be better equipped to help your organisation operationalise AI models, reduce the gap between experimentation and production, improve model reliability, and deliver AI solutions that are scalable, monitored and aligned with business needs.