MLOps Foundations: CI/CD for Machine Learning Pipelines (SF)
Training Provider: NTUC LEARNINGHUB PTE. LTD.
Course Reference: TGS-2026062540
S$1,750
Original: S$3,500
Save S$1,750
About This Course
This 5-day hands-on course enables data scientists, ML engineers, and DevOps professionals to build end-to-end MLOps pipelines. It covers the automation of ML workflows using CI/CD principles, containerization with Docker, deployment via Kubernetes, model testing, versioning, and monitoring. Learners implement real-world pipelines that include Git-based orchestration, automated validation, and live monitoring dashboards. The course emphasizes scalability, reproducibility, and automation best practices essential to modern ML operations.
What You'll Learn
Learning Outcomes:
LO1. Evaluate the feasibility of various AI applications across the product development track and maintenance processes to determine their technical applicability.
LO2. Correlate algorithm design choices with operational efficiency to determine technical implementation strategies for AI applications.
LO3. Compare various AI applications by evaluating their strengths and limitations against standard process improvement methods to determine the most effective solution for product management.
LO4. Analyze AI application algorithms by deconstructing their design and implementation to identify specific performance and logic characteristics.
LO5. Assess AI-driven product improvements by applying performance effectiveness and analysis concepts to determine technical and business impact.
LO6. Identify technical strengths and limitations through the application of standardized evaluation methodologies to evaluate AI application effectiveness.
LO1. Evaluate the feasibility of various AI applications across the product development track and maintenance processes to determine their technical applicability.
LO2. Correlate algorithm design choices with operational efficiency to determine technical implementation strategies for AI applications.
LO3. Compare various AI applications by evaluating their strengths and limitations against standard process improvement methods to determine the most effective solution for product management.
LO4. Analyze AI application algorithms by deconstructing their design and implementation to identify specific performance and logic characteristics.
LO5. Assess AI-driven product improvements by applying performance effectiveness and analysis concepts to determine technical and business impact.
LO6. Identify technical strengths and limitations through the application of standardized evaluation methodologies to evaluate AI application effectiveness.
Entry Requirements
• The candidate should hold a diploma and have at least 2 years of experience working as a developer/programmer.
• Candidates who do not meet the above requirements will be assessed on case to case basis
Course Details
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