Prompt Engineering for Healthcare Training Course

Vertex AI delivers advanced tools for fine-tuning large-scale models and managing prompts, empowering development and data teams to enhance model accuracy, streamline iteration workflows, and uphold rigorous evaluation standards using built-in libraries and services.

This instructor-led, live training (available online or on-site) is designed for intermediate to advanced practitioners seeking to improve the performance and reliability of generative AI applications through supervised fine-tuning, prompt versioning, and evaluation capabilities within Vertex AI.

By the conclusion of this training, participants will be able to:

• Apply supervised fine-tuning techniques to Gemini models within Vertex AI.
• Implement prompt management workflows, including version control and testing.
• Leverage evaluation libraries to benchmark and optimise AI performance.
• Deploy and monitor enhanced models in production environments.

Course Format

• Interactive lectures and group discussions.
• Practical labs featuring Vertex AI fine-tuning and prompt management tools.
• Real-world case studies showcasing enterprise model optimisation.

Course Customisation Options

• To request a customised training session for this course, please contact us to arrange.

Agentic AI is an approach where AI systems plan, reason, and take tool-using actions to accomplish goals within defined constraints.

This instructor-led, live training (online or onsite) is aimed at intermediate-level healthcare and data teams who wish to design, evaluate, and govern agentic AI solutions for clinical and operational use cases.

By the end of this training, participants will be able to:

• Explain agentic AI concepts and constraints in healthcare contexts.
• Design safe agent workflows with planning, memory, and tool usage.
• Build retrieval-augmented agents over clinical documents and knowledge bases.
• Evaluate, monitor, and govern agent behaviour with guardrails and human-in-the-loop controls.

Format of the Course

• Interactive lecture and facilitated discussion.
• Guided labs and code walkthroughs in a sandbox environment.
• Scenario-based exercises on safety, evaluation, and governance.

Course Customisation Options

• To request a customised training for this course, please contact us to arrange.

This instructor-led, live training in New Zealand (online or onsite) is aimed at intermediate-level to advanced-level healthcare professionals and AI developers who wish to implement AI-driven healthcare solutions.

By the end of this training, participants will be able to:

• Understand the role of AI agents in healthcare and diagnostics.
• Develop AI models for medical image analysis and predictive diagnostics.
• Integrate AI with electronic health records (EHR) and clinical workflows.
• Ensure compliance with healthcare regulations and ethical AI practices.

This instructor-led, live training in New Zealand (online or on-site) is designed for intermediate-level healthcare professionals who wish to apply AI and AR/VR solutions in medical training, surgical simulations, and rehabilitation.

By the end of this training, participants will be able to:

• Understand the role of AI in enhancing AR/VR experiences within healthcare.
• Use AR/VR for surgical simulations and medical training.
• Apply AR/VR tools in patient rehabilitation and therapy.
• Explore ethical and privacy concerns related to AI-enhanced medical tools.

This instructor-led, live training in New Zealand (online or onsite) is aimed at intermediate-level data scientists and healthcare professionals who wish to leverage AI for advanced healthcare applications using Google Colab.

By the end of this training, participants will be able to:

• Implement AI models for healthcare using Google Colab.
• Use AI for predictive modelling in healthcare data.
• Analyse medical images with AI-driven techniques.
• Explore ethical considerations in AI-based healthcare solutions.

This instructor-led, live training in New Zealand (online or on-site) is designed for intermediate-level healthcare professionals and data scientists who wish to understand and apply AI technologies within healthcare environments.

By the end of this training, participants will be able to:

• Identify key healthcare challenges that AI can address.
• Analyse AI’s impact on patient care, safety, and medical research.
• Understand the relationship between AI and healthcare business models.
• Apply fundamental AI concepts to healthcare scenarios.
• Develop machine learning models for medical data analysis.

This instructor-led, live training in New Zealand (available online or on-site) is designed for healthcare professionals and researchers who wish to harness ChatGPT to improve patient care, streamline workflows, and enhance healthcare outcomes.

By the end of this training, participants will be able to:

• Understand the fundamentals of ChatGPT and its applications in healthcare.
• Leverage ChatGPT to automate healthcare processes and interactions.
• Provide accurate medical information and support to patients using ChatGPT.
• Apply ChatGPT for medical research and analysis.

This instructor-led, live training in New Zealand (delivered online or on-site) is designed for intermediate-level healthcare professionals, biomedical engineers, and AI developers who wish to harness Edge AI to create innovative healthcare solutions.

By the conclusion of this training, participants will be able to:

• Understand the role and benefits of Edge AI in healthcare.
• Develop and deploy AI models on edge devices for healthcare applications.
• Implement Edge AI solutions in wearable devices and diagnostic tools.
• Design and deploy patient monitoring systems using Edge AI.
• Address ethical and regulatory considerations in healthcare AI applications.

This instructor-led, live training in New Zealand (online or on-site) is designed for intermediate to advanced-level medical AI developers and data scientists who wish to fine-tune models for clinical diagnosis, disease prediction, and patient outcome forecasting using both structured and unstructured medical data.

By the end of this training, participants will be able to:

• Fine-tune AI models on healthcare datasets, including electronic medical records (EMRs), medical imaging, and time-series data.
• Apply transfer learning, domain adaptation, and model compression techniques within medical contexts.
• Address privacy concerns, bias mitigation, and regulatory compliance during model development.
• Deploy and monitor fine-tuned models in real-world healthcare environments.

Generative AI is a technology that creates new content such as text, images, and recommendations based on prompts and data.

This instructor-led, live training (online or on-site) is designed for beginner to intermediate-level healthcare professionals who wish to leverage generative AI and prompt engineering to enhance efficiency, accuracy, and communication within medical contexts.

By the end of this training, participants will be able to:

• Understand the fundamentals of generative AI and prompt engineering.
• Apply AI tools to streamline clinical, administrative, and research tasks.
• Ensure ethical, safe, and compliant use of AI in healthcare.
• Optimise prompts to achieve consistent and accurate results.

Format of the Course

• Interactive lecture and discussion.
• Practical exercises and case studies.
• Hands-on experimentation with AI tools.

Course Customisation Options

• To request a customised training for this course, please contact us to arrange.

This instructor-led, live training in New Zealand (delivered either online or on-site) is designed for healthcare professionals, data analysts, and policy makers at beginner to intermediate levels who wish to understand and apply generative AI within the healthcare context.

By the conclusion of this training, participants will be able to:

• Explain the core principles and practical applications of generative AI in healthcare.
• Identify opportunities where generative AI can enhance drug discovery and support personalised medicine.
• Apply generative AI techniques to medical imaging and diagnostic processes.
• Evaluate the ethical implications of deploying AI in clinical and medical settings.
• Develop effective strategies for integrating AI technologies into existing healthcare systems.

LangGraph enables stateful, multi-actor workflows powered by large language models (LLMs), offering precise control over execution paths and state persistence. In the healthcare sector, these capabilities are vital for ensuring compliance, interoperability, and the development of decision-support systems that align seamlessly with clinical workflows.

This instructor-led, live training (available online or on-site) is designed for intermediate to advanced-level professionals who wish to design, implement, and manage LangGraph-based healthcare solutions while navigating regulatory, ethical, and operational challenges.

By the end of this training, participants will be able to:

• Design healthcare-specific LangGraph workflows with compliance and auditability as core considerations.
• Integrate LangGraph applications with medical ontologies and standards such as FHIR, SNOMED CT, and ICD.
• Apply best practices for reliability, traceability, and explainability within sensitive healthcare environments.
• Deploy, monitor, and validate LangGraph applications in real-world healthcare production settings.

Course Format

• Interactive lectures and group discussions.
• Hands-on exercises featuring real-world case studies.
• Practical implementation in a live-lab environment.

Course Customisation Options

• To request a customised version of this training, please contact us to arrange a tailored session.

This instructor-led, live training in New Zealand (online or on-site) is designed for intermediate to advanced-level healthcare professionals, medical researchers, and AI developers who wish to apply multimodal AI in medical diagnostics and healthcare applications.

By the end of this training, participants will be able to:

• Understand the role of multimodal AI in modern healthcare.
• Integrate structured and unstructured medical data for AI-driven diagnostics.
• Apply AI techniques to analyse medical images and electronic health records.
• Develop predictive models for disease diagnosis and treatment recommendations.
• Implement speech and natural language processing (NLP) for medical transcription and patient interaction.

Ollama is a lightweight platform for running large language models locally.

This instructor-led, live training (online or on-site) is designed for intermediate-level healthcare practitioners and IT teams who wish to deploy, customise, and operationalise Ollama-based AI solutions within clinical and administrative settings.

Upon completing this training, participants will be able to:

• Install and configure Ollama for secure use in healthcare environments.
• Integrate local LLMs into clinical workflows and administrative processes.
• Customise models for healthcare-specific terminology and tasks.
• Apply best practices for privacy, security, and regulatory compliance.

Course Format

• Interactive lectures and discussions.
• Hands-on demonstrations and guided exercises.
• Practical implementation within a sandboxed healthcare simulation environment.

Course Customisation Options

• To request a customised version of this training, please contact us to arrange.

TinyML refers to the integration of machine learning into low-power, resource-constrained wearable and medical devices.

This instructor-led, live training (available online or on-site) is designed for intermediate-level practitioners who wish to implement TinyML solutions for healthcare monitoring and diagnostic applications.

Upon completing this training, participants will be able to:

• Design and deploy TinyML models for real-time health data processing.
• Collect, preprocess, and interpret biosensor data to generate AI-driven insights.
• Optimise models for low-power and memory-constrained wearable devices.
• Evaluate the clinical relevance, reliability, and safety of outputs generated by TinyML systems.

Course Format

• Lectures supported by live demonstrations and interactive discussion.
• Hands-on practice using wearable device data and TinyML frameworks.
• Implementation exercises within a guided lab environment.

Course Customisation Options

• For tailored training aligned with specific healthcare devices or regulatory workflows, please contact us to customise the programme.