StepMerit Learning

Adelaide, Australia

Generative AI

MLOps & LLMOpsin Adelaide

Explore MLOps & LLMOps training to master machine learning deployment, model lifecycle management, CI/CD automation, and large language model operations. Learn practical techniques for experiment tracking, model monitoring, version control…

40 Hours/5 daysLive virtual trainingIntermediate

Classroom training and venue arrangements depend on the availability of a suitable conference hall in Adelaide, Australia. Dates, seating capacity, facilities and the final venue are confirmed before booking.

Learning in Adelaide, Australia

City details

Adelaide combines a high-quality learning environment, cultural diversity and active professional communities.

City speciality

Education, liveability and business communities

Classroom availability and venue

Classroom training and venue arrangements depend on the availability of a suitable conference hall in Adelaide, Australia. Dates, seating capacity, facilities and the final venue are confirmed before booking.

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Explore MLOps & LLMOps

Explore MLOps & LLMOps training to master machine learning deployment, model lifecycle management, CI/CD automation, and large language model operations. Learn practical techniques for experiment tracking, model monitoring, version control, scalable AI deployment, and production-ready Generative AI workflows. Develop skills to streamline ML pipelines and manage reliable AI applications. Join StepMerit's MLOps & LLMOps course in Adelaide, Australia, to strengthen your expertise in AI engineering and operationalise machine learning solutions.

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Your learning location

Learning from Adelaide

Adelaide combines a high-quality learning environment, cultural diversity and active professional communities.

Course curriculum

  • Module 1: Introduction to MLOps and LLMOps
    • Understanding MLOps and LLMOps concepts, objectives, and applications.
    • Comparing traditional software development, machine learning operations, and LLM operations.
    • Exploring the AI lifecycle from experimentation to production.
    • Identifying operational challenges in machine learning and Generative AI projects.
  • Module 2: Machine Learning Lifecycle and Workflow Design
    • Understanding data preparation, model development, validation, and deployment stages.
    • Designing repeatable machine learning workflows.
    • Managing datasets, model artefacts, and experiment configurations.
    • Establishing workflow standards for collaborative AI development.
  • Module 3: Development Environment and Version Control
    • Setting up Python environments for ML and LLM applications.
    • Using Git and Git-based repositories for source code management.
    • Managing dependencies and environment configurations.
    • Tracking changes to code, configuration files, and project artefacts.
  • Module 4: Data and Experiment Management
    • Preparing datasets for machine learning pipelines.
    • Tracking experiment parameters, metrics, and outputs.
    • Understanding dataset versioning and data lineage.
    • Comparing experiment results to support model selection.
    • Exploring tools such as MLflow for experiment tracking.
  • Module 5: Model Training and Reproducibility
    • Building repeatable model training workflows.
    • Managing training configurations, checkpoints, and artefacts.
    • Recording model parameters and evaluation metrics.
    • Identifying reproducibility challenges across development environments.
    • Documenting training processes for collaboration and review.
  • Module 6: Model Packaging and Containerisation
    • Understanding Docker images, containers, and registries.
    • Packaging ML models and their dependencies.
    • Creating consistent environments for development and deployment.
    • Configuring environment variables and application settings.
    • Troubleshooting containerisation and dependency issues.
  • Module 7: CI/CD for Machine Learning Applications
    • Understanding continuous integration and continuous delivery principles.
    • Automating code validation, testing, and build processes.
    • Integrating model validation into deployment pipelines.
    • Managing deployment configurations and release artefacts.
    • Exploring CI/CD workflows for ML application delivery.
  • Module 8: ML Pipeline Orchestration
    • Understanding pipeline components, dependencies, and execution stages.
    • Automating data processing, training, and evaluation tasks.
    • Managing pipeline scheduling, failures, and retries.
    • Exploring orchestration tools and workflow automation patterns.
    • Designing repeatable end-to-end ML pipelines.
  • Module 9: Model Deployment and Serving
    • Understanding batch inference and online model serving.
    • Deploying models through APIs and application endpoints.
    • Configuring model servers and inference environments.
    • Testing prediction services and handling errors.
    • Evaluating latency, throughput, and resource requirements.
  • Module 10: Model Monitoring and Maintenance
    • Monitoring model performance and operational health.
    • Understanding data drift, concept drift, and model degradation.
    • Tracking inference latency, errors, and resource utilisation.
    • Establishing alerts and model review procedures.
    • Planning model updates and retraining workflows.
  • Module 11: Introduction to LLMOps
    • Understanding the operational requirements of large language models.
    • Managing LLM application configurations and model versions.
    • Exploring hosted and self-managed LLM deployment approaches.
    • Identifying challenges related to latency, token usage, and inference costs.
    • Designing maintainable LLM application workflows.
  • Module 12: Prompt and Dataset Management for LLMs
    • Organising and versioning prompt templates.
    • Creating representative datasets for LLM application testing.
    • Managing system instructions and model-specific configurations.
    • Evaluating prompt changes against expected outputs.
    • Maintaining records of prompt experiments and results.
  • Module 13: LLM Evaluation and Quality Testing
    • Establishing evaluation criteria for LLM-generated responses.
    • Testing relevance, factual consistency, instruction following, and response quality.
    • Exploring automated metrics and human evaluation methods.
    • Building regression test suites for LLM applications.
    • Evaluating failure cases, hallucinations, and inconsistent outputs.
  • Module 14: LLM Observability, Security, and Governance
    • Monitoring request traces, model responses, latency, and token consumption.
    • Understanding logging, tracing, and observability for LLM applications.
    • Applying data protection, access control, and secure configuration practices.
    • Managing sensitive information in prompts and application logs.
    • Documenting model limitations, governance requirements, and audit considerations.
  • Module 15: LLM Application Deployment and Optimisation
    • Deploying LLM-powered applications in suitable environments.
    • Integrating model APIs, retrieval components, and application services.
    • Exploring caching, batching, and other inference optimisation techniques.
    • Assessing deployment scalability and infrastructure requirements.
    • Reviewing cost-performance trade-offs for LLM applications.
  • Module 16: Capstone Project and Practical Implementation
    • Designing an end-to-end MLOps pipeline for a machine learning application.
    • Implementing version control, experiment tracking, testing, and deployment.
    • Developing an LLM application workflow with prompt evaluation and monitoring.
    • Documenting pipeline configurations, test results, and operational limitations.
    • Presenting the project with implementation evidence and improvement recommendations.
  • Training Delivery and Practical Learning
    • Instructor-led sessions covering MLOps and LLMOps concepts.
    • Hands-on exercises using relevant tools and development environments.
    • Guided implementation of model lifecycle and LLM application workflows.
    • Project-based learning focused on practical AI engineering challenges.
    • StepMerit training support for learners in Adelaide, Australia.

Exam details

  • Exam Level: Intermediate – MLOps & LLMOps.
  • Exam Type: Online computer-based examination consisting of multiple-choice questions and scenario-based questions.
  • Number of Questions: 40 questions.
  • Exam Duration: 90 minutes.
  • Passing Score: 70% (28 out of 40 questions).
  • Exam Delivery: Online assessment through the StepMerit learning and assessment platform.
  • Practical Assessment: Learners may be evaluated through practical exercises involving ML pipeline configuration, model deployment workflows, experiment tracking, and LLM application evaluation.
  • Assessment Methodology: The assessment may include conceptual questions, workflow analysis, troubleshooting scenarios, and interpretation of model performance or monitoring results.
  • Certification: Learners who satisfy the assessment requirements may receive a StepMerit Certificate of Course Completion.
  • Exam Preparation: Learners should review the course materials, practise relevant MLOps and LLMOps workflows, and complete hands-on exercises.
  • Training and Assessment Support: StepMerit provides course-related learning support for participants in Adelaide, Australia.

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Who this course is for

Machine Learning Engineers MLOps Engineers and Professionals LLMOps Engineers and Generative AI Specialists Data Scientists and Data Analysts AI Engineers and AI Application Developers Software Engineers and Python Developers DevOps Engineers and Site Reliability Engineers (SREs) Cloud Engineers and AI Platform Engineers Deep Learning Engineers and Researchers Generative AI Developers and LLM Application Builders Data Engineers and Machine Learning Platform Teams IT Professionals Transitioning into AI Engineering Technical Leads and AI Solution Architects Professionals Working on Enterprise AI Deployment and Monitoring Students and Graduates with a Foundation in Python and Machine Learning…

Before you begin

Basic Python Programming: Familiarity with Python syntax, functions, libraries, and scripting for machine learning and AI workflows. Machine Learning Fundamentals: Understanding of supervised learning, unsupervised learning, model training, validation, and performance evaluation. Deep Learning Knowledge: Basic awareness of neural networks, training processes, and frameworks such as PyTorch or TensorFlow. Generative AI and LLM Fundamentals: Understanding of large language models, prompts, embeddings, tokenisation, and AI application development. Software Development and Git: Familiarity with version control, Git repositories, code management, and collaborative development practices. Linux and…

Learning outcomes

Project Overview The MLOps & LLMOps training equips learners with practical skills to deploy, monitor, evaluate, and maintain machine learning models and large language model (LLM) applications in production environments. In this banking scenario, learners apply these skills to develop a reliable AI-powered loan risk assessment and customer support solution, focusing on automation, model performance, operational monitoring, security, and responsible AI practices. Problem Statement A banking organisation faced challenges in managing loan risk prediction models and AI-powered customer support applications. Manual model deployment processes caused delays, while inconsistent experiment tracking…

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