Artificial intelligence is transforming industries through predictive analytics, automation, intelligent assistants, and Generative AI applications. However, developing an AI model is only one part of the process. Organisations must also deploy, monitor, evaluate, secure, and maintain AI systems to ensure reliable performance in production environments. This is where MLOps and LLMOps become essential.
MLOps, or Machine Learning Operations, combines machine learning, software development, and operational practices to streamline the machine learning lifecycle. LLMOps, or Large Language Model Operations, extends operational principles to large language models and Generative AI applications, including prompt management, response evaluation, monitoring, and model optimisation.
The MLOps & LLMOps training course by StepMerit helps learners understand how to manage AI workflows efficiently, automate deployment processes, improve model reliability, and build maintainable AI applications through practical exercises and real-world scenarios.
Key Topics Covered in MLOps & LLMOps Training
The course introduces essential tools, techniques, and workflows for operationalising machine learning and Generative AI solutions.
- Machine Learning Lifecycle Management: Understand data preparation, model training, validation, deployment, and maintenance.
- Experiment Tracking and Model Versioning: Learn how tools such as MLflow help track experiments, evaluation metrics, parameters, and model artefacts.
- Containerisation and Deployment: Explore Docker and model-serving approaches for packaging and deploying AI applications consistently.
- CI/CD for Machine Learning: Understand automated testing, validation, deployment pipelines, and controlled model updates.
- Model Monitoring and Maintenance: Learn to identify performance degradation, data drift, service failures, and latency issues.
- LLM Application Operations: Explore prompt versioning, dataset management, LLM evaluation, observability, and production monitoring.
- AI Security and Governance: Understand access controls, sensitive-data protection, audit logging, and responsible AI practices.
Benefits of Attending the MLOps & LLMOps Course
Developing operational AI skills can help professionals bridge the gap between experimentation and production deployment.
1. Build Practical AI Engineering Skills
Understand how machine learning models and LLM applications move from development environments into production systems.
2. Automate Machine Learning Workflows
Learn the principles of pipeline automation, experiment tracking, testing, and repeatable deployments to reduce manual operational effort.
3. Improve Model Reliability
Explore monitoring, version control, evaluation, and maintenance practices that help teams identify issues and manage changing model performance.
4. Strengthen Generative AI Development Knowledge
Understand how to assess LLM responses, manage prompts, evaluate application quality, and monitor AI-powered services.
5. Improve Collaboration Across Technical Teams
Learn how standardised workflows, documented configurations, and reproducible environments support collaboration between data science, development, and operations teams.
6. Prepare for Relevant Career Opportunities
The course supports skill development relevant to roles such as MLOps Engineer, Machine Learning Engineer, LLMOps Engineer, AI Engineer, and AI Platform Engineer. Employment outcomes depend on experience, practical skills, and market requirements.
Case Study: Implementing MLOps and LLMOps in Banking
Business Challenge
A banking organisation wanted to improve its loan risk assessment process and customer support services using AI. However, its machine learning models were difficult to update consistently, and changes in financial data could affect prediction quality. The bank also needed to ensure that its AI-powered customer support assistant provided appropriate answers while protecting confidential customer information.
Manual deployments, limited experiment tracking, and inconsistent evaluation procedures created operational challenges.
Implementation Approach
The project team designed an integrated MLOps and LLMOps workflow.
- Automated ML Pipeline: Created a repeatable workflow for data validation, model training, testing, and deployment.
- Experiment Tracking: Used MLflow to record model versions, parameters, performance metrics, and experiment artefacts.
- Controlled Deployment: Applied containerisation and CI/CD concepts to standardise testing and model releases.
- Continuous Monitoring: Defined monitoring procedures for model performance, data drift, application latency, and service errors.
- LLM Evaluation: Developed test cases to assess the relevance, factual accuracy, and consistency of responses generated by a banking support assistant.
- Security and Governance: Incorporated access controls, sensitive-data protection, audit logging, and human review for high-impact lending decisions.
Outcome and Learning Evidence
The proposed implementation established a structured approach to managing both predictive ML models and LLM-based customer support applications. It enabled repeatable deployment procedures, documented evaluation practices, and clearer monitoring requirements. The project also demonstrated how security and human oversight can be incorporated into AI workflows.
Learners can demonstrate their skills through pipeline configurations, experiment records, deployment documentation, sample monitoring metrics, LLM evaluation reports, and security checklists. These are expected project deliverables rather than verified results from a named banking deployment.
MLOps & LLMOps Exam Details
The course assessment can evaluate learners' understanding of machine learning operations, LLM application management, deployment workflows, monitoring, and troubleshooting.
- Exam Level: Intermediate – MLOps & LLMOps
- Exam Type: Online computer-based assessment with multiple-choice and scenario-based questions
- Number of Questions: 40
- Exam Duration: 90 minutes
- Proposed Passing Score: 70% (28 out of 40)
- Assessment Topics: ML lifecycle management, experiment tracking, CI/CD, model deployment, monitoring, LLM evaluation, security, and governance
- Certification: Eligible learners may receive a StepMerit Certificate of Course Completion, subject to programme requirements.
Who Should Attend This Course?
The MLOps & LLMOps course is suitable for machine learning engineers, data scientists, AI developers, Python programmers, DevOps professionals, cloud engineers, and technical professionals seeking to develop production AI skills. Basic Python and machine learning knowledge are recommended to get the most from the training.
Conclusion
MLOps and LLMOps are important disciplines for organisations that want to deploy AI systems reliably and manage them throughout their lifecycle. From experiment tracking and automated pipelines to LLM evaluation, monitoring, and governance, these practices help technical teams build more maintainable and observable AI applications.
Through StepMerit's MLOps & LLMOps training, learners can develop practical knowledge of modern AI operations, explore an industry-relevant banking scenario, and build project evidence that supports their professional development. The course provides a foundation for contributing to machine learning deployment and Generative AI operations projects across a range of industries.