LangGraph in Healthcare: Workflow Orchestration for Regulated Environments Training Course

LangGraph is a framework for building stateful, multi-actor LLM applications as composable graphs with persistent state and control over execution.

This instructor-led, live training (available online or on-site) is designed for advanced-level AI platform engineers, AI-focused DevOps professionals, and ML architects who aim to optimise, debug, monitor, and operate production-grade LangGraph systems.

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

• Design and optimise complex LangGraph topologies for speed, cost-efficiency, and scalability.
• Engineer reliability through retries, timeouts, idempotency, and checkpoint-based recovery.
• Debug and trace graph executions, inspect state, and systematically reproduce production issues.
• Instrument graphs with logs, metrics, and traces, deploy to production, and monitor SLAs and costs.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and hands-on practice.
• Real-world implementation in a live-lab environment.

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.

LangGraph is a framework for building stateful, multi-actor LLM applications as composable graphs with persistent state and control over execution.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to design, implement, and operate LangGraph-based finance solutions with proper governance, observability, and compliance.

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

• Design finance-specific LangGraph workflows aligned to regulatory and audit requirements.
• Integrate financial data standards and ontologies into graph state and tooling.
• Implement reliability, safety, and human-in-the-loop controls for critical processes.
• Deploy, monitor, and optimise LangGraph systems for performance, cost, and SLAs.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

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

LangGraph is a framework for building graph-structured LLM applications that support planning, branching, tool use, memory, and controllable execution.

This instructor-led, live training (online or onsite) is aimed at beginner-level developers, prompt engineers, and data practitioners who wish to design and build reliable, multi-step LLM workflows using LangGraph.

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

• Explain core LangGraph concepts (nodes, edges, state) and when to use them.
• Build prompt chains that branch, call tools, and maintain memory.
• Integrate retrieval and external APIs into graph workflows.
• Test, debug, and evaluate LangGraph apps for reliability and safety.

Course Format

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

Course Customisation Options

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

LangGraph is a framework for building stateful, multi-actor LLM applications as composable graphs with persistent state and precise control over execution.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to design, implement, and operate LangGraph-based legal solutions with the necessary compliance, traceability, and governance controls.

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

• Design legal-specific LangGraph workflows that preserve auditability and compliance.
• Integrate legal ontologies and document standards into graph state and processing.
• Implement guardrails, human-in-the-loop approvals, and traceable decision paths.
• Deploy, monitor, and maintain LangGraph services in production with observability and cost controls.

Format of the Course

• Interactive lecture and discussion.
• Plenty of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

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

LangGraph is a framework for composing graph-structured LLM workflows that support branching, tool use, memory, and controllable execution.

This instructor-led, live training (online or on-site) is aimed at intermediate-level engineers and product teams who wish to combine LangGraph's graph logic with LLM agent loops to build dynamic, context-aware applications such as customer support agents, decision trees, and information retrieval systems.

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

• Design graph-based workflows that coordinate LLM agents, tools, and memory.
• Implement conditional routing, retries, and fallbacks for robust execution.
• Integrate retrieval, APIs, and structured outputs into agent loops.
• Evaluate, monitor, and harden agent behaviour for reliability and safety.

Format of the Course

• Interactive lecture and facilitated discussion.
• Guided labs and code walkthroughs in a sandbox environment.
• Scenario-based design exercises and peer reviews.

Course Customisation Options

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

LangGraph is a graph-based orchestration framework that enables conditional, multi-step LLM and tool workflows, ideal for automating and personalising content pipelines.

This instructor-led, live training (online or on-site) is aimed at intermediate-level marketers, content strategists, and automation developers who wish to implement dynamic, branching email campaigns and content generation pipelines using LangGraph.

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

• Design graph-structured content and email workflows with conditional logic.
• Integrate LLMs, APIs, and data sources for automated personalisation.
• Manage state, memory, and context across multi-step campaigns.
• Evaluate, monitor, and optimise workflow performance and delivery outcomes.

Format of the Course

• Interactive lectures and group discussions.
• Hands-on labs implementing email workflows and content pipelines.
• Scenario-based exercises on personalisation, segmentation, and branching logic.

Course Customisation Options

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