Deploying and Operating AI Solutions
Training Provider: NATIONAL UNIVERSITY OF SINGAPORE
Course Reference: TGS-2026062339
S$855
Original: S$2,850
Save S$1,995
About This Course
The rapid advancements in machine learning (ML) and large language models (LLM) have unlocked unprecedented opportunities for businesses and organizations across various sectors. However, the journey from promising models in research labs to impactful, reliable, and scalable solutions in real-world applications presents significant challenges. This course in MLOps (Machine Learning Operations) and LLMOps (Large Language Model Operations) directly addresses these challenges by equipping learners with the essential principles, practices, and tools to streamline the entire ML/LLM lifecycle.
What You'll Learn
Foundational Concepts: Understand the core fundamentals of MLOps, its key components and able to differentiate among Development Operations vs. MLOps vs. LLMOps in terms of their objectives, characteristics, challenges, approaches, etc.
• MLOps Design: Design the pipelines for the Continuous Integration and Continuous Deployment (CI/CD) of ML models and their operational data by leveraging on MLOps platforms and best practices including security considerations.
• MLOps Conduct: Conduct the tasks of the pipeline of a ML model and its operational data.
• MLOps Monitoring: Monitor and evaluate the operation of the pipeline of a ML model and its operational data.
• LLMOps Design: Design the pipelines for the Continuous Integration and Continuous Deployment (CI/CD) of LLMs by leveraging on LLMOps platforms and best practices including security considerations.
• LLMOps Conduct: Conduct the tasks of the pipeline of an LLM and its operational data.
• LLMOps Monitoring: Monitor and evaluate the operation of the pipeline of an LLM and its operational data."
• MLOps Design: Design the pipelines for the Continuous Integration and Continuous Deployment (CI/CD) of ML models and their operational data by leveraging on MLOps platforms and best practices including security considerations.
• MLOps Conduct: Conduct the tasks of the pipeline of a ML model and its operational data.
• MLOps Monitoring: Monitor and evaluate the operation of the pipeline of a ML model and its operational data.
• LLMOps Design: Design the pipelines for the Continuous Integration and Continuous Deployment (CI/CD) of LLMs by leveraging on LLMOps platforms and best practices including security considerations.
• LLMOps Conduct: Conduct the tasks of the pipeline of an LLM and its operational data.
• LLMOps Monitoring: Monitor and evaluate the operation of the pipeline of an LLM and its operational data."
Entry Requirements
General Degree
Course Details
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