LangGraph in Healthcare: Workflow Orchestration for Regulated Environments Training Course

LangGraph is a framework designed for constructing stateful, multi-actor LLM applications as composable graphs, featuring persistent state and execution control.

This instructor-led live training, available online or onsite, targets advanced AI platform engineers, AI DevOps specialists, and ML architects aiming to optimize, debug, monitor, and operate production-grade LangGraph systems.

Upon completing this training, participants will be able to:

• Design and optimize complex LangGraph topologies for speed, cost-efficiency, and scalability.
• Ensure 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 lecture and discussion.
• Ample exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

Agentic AI refers to a methodology where AI systems independently plan, reason, and utilize tools to achieve specific objectives within set boundaries.

This instructor-led, live training (available online or onsite) targets intermediate-level healthcare and data teams looking to design, evaluate, and govern agentic AI solutions for clinical and operational applications.

Upon completing this training, participants will be equipped to:

• Articulate agentic AI concepts and constraints within healthcare settings.
• Construct secure agent workflows incorporating planning, memory, and tool integration.
• Develop retrieval-augmented agents that process clinical documents and knowledge bases.
• Assess, monitor, and govern agent behaviour using guardrails and human-in-the-loop controls.

Course Format

• Interactive lectures complemented by facilitated discussions.
• Guided labs and code walkthroughs conducted in a sandbox environment.
• Scenario-based exercises focusing on safety, evaluation, and governance.

Customization Options

• To request customized training for this course, please get in touch with us to arrange your session.

This instructor-led, live training in Nigeria (online or onsite) is designed for intermediate to advanced healthcare professionals and AI developers who want to implement AI-driven healthcare solutions.

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

• Grasp 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 live, instructor-led training in Nigeria (online or on-site) is designed for intermediate-level healthcare professionals aiming to apply AI and AR/VR solutions for medical training, surgical simulations, and rehabilitation.

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

• Understand how AI enhances AR/VR experiences in healthcare.
• Use AR/VR for surgery simulations and medical training.
• Apply AR/VR tools in patient rehabilitation and therapy.
• Explore the ethical and privacy concerns in AI-enhanced medical tools.

This live, instructor-led training in Nigeria (online or onsite) targets intermediate data scientists and healthcare professionals eager to utilize AI for advanced medical applications via Google Colab.

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

• Deploy AI models for healthcare solutions using Google Colab.
• Apply AI for predictive modeling on healthcare data.
• Analyze medical imagery using AI-driven techniques.
• Examine the ethical implications of AI in healthcare.

This instructor-led, live training in Nigeria (online or onsite) is aimed at intermediate-level healthcare professionals and data scientists who wish to understand and apply AI technologies in healthcare environments.

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

• Identify key healthcare challenges that AI can address.
• Analyze 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 Nigeria (online or onsite) is designed for healthcare professionals and researchers aiming to harness ChatGPT to elevate patient care, optimize workflows, and enhance healthcare outcomes.

Upon completing this training, participants will be equipped to:

• Grasp the core concepts of ChatGPT and its healthcare applications.
• Employ ChatGPT to automate healthcare processes and patient interactions.
• Deliver precise medical information and support to patients via ChatGPT.
• Apply ChatGPT in medical research and analytical tasks.

This instructor-led, live training in Nigeria (online or onsite) is aimed at intermediate-level healthcare professionals, biomedical engineers, and AI developers who wish to leverage Edge AI for innovative healthcare solutions.

By the end 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 Nigeria (online or onsite) is designed for intermediate to advanced medical AI developers and data scientists aiming to fine-tune models for clinical diagnosis, disease prediction, and patient outcome forecasting using structured and unstructured medical data.

Upon completing this training, participants will be equipped to:

• Fine-tune AI models on healthcare datasets, including Electronic Medical Records (EMRs), imaging, and time-series data.
• Utilise transfer learning, domain adaptation, and model compression techniques within medical contexts.
• Navigate privacy concerns, bias mitigation, and regulatory compliance during model development.
• Deploy and monitor fine-tuned models in practical healthcare settings.

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 onsite) is aimed at beginner-level to intermediate-level healthcare professionals who wish to use generative AI and prompt engineering to improve efficiency, accuracy, and communication in medical contexts.

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

• Grasp the core concepts of generative AI and prompt engineering.
• Leverage AI tools to streamline clinical, administrative, and research tasks.
• Uphold ethical, safe, and compliant use of AI in healthcare.
• Refine prompts to achieve consistent and accurate results.

Course Format

• Interactive lectures and discussions.
• Practical exercises and case studies.
• Hands-on experimentation with AI tools.

Course Customization Options

• For bespoke training on this course, please contact us to arrange.

LangGraph is a framework designed for constructing 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 optimize 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 Customization Options

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

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

This instructor-led live training (available online or on-site) targets beginner-level developers, prompt engineers, and data practitioners who want to design and build reliable, multi-step LLM workflows using LangGraph.

By the conclusion of this training, participants will be capable of:

• Explaining core LangGraph concepts (nodes, edges, state) and understanding when to apply them.
• Constructing prompt chains that branch, invoke tools, and maintain memory.
• Integrating retrieval mechanisms and external APIs into graph workflows.
• Testing, debugging, and evaluating LangGraph applications to ensure reliability and safety.

Course Format

• Interactive lectures and facilitated discussions.
• Guided labs and code walkthroughs within a sandbox environment.
• Scenario-based exercises focusing on design, testing, and evaluation.

Course Customization Options

• To request customized training for this course, please contact us to make arrangements.

LangGraph serves as a framework for constructing stateful, multi-actor LLM applications by composing them into graphs that maintain persistent state and offer precise control over execution.

This instructor-led live training, available either online or onsite, is designed for intermediate to advanced professionals seeking to design, implement, and operate LangGraph-based legal solutions with the necessary compliance, traceability, and governance controls.

Upon completing this training, participants will be able to:

• Design legal-specific LangGraph workflows that ensure 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.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

LangGraph serves as a framework designed for composing graph-structured LLM workflows that facilitate branching, tool utilization, memory management, and controlled execution.

This instructor-led live training, available either online or onsite, targets intermediate-level engineers and product teams looking to merge LangGraph’s graph logic with LLM agent loops. The goal is to develop dynamic, context-aware applications such as customer support agents, decision trees, and information retrieval systems.

Upon completing this training, participants will be equipped to:

• Design graph-based workflows that effectively coordinate LLM agents, tools, and memory.
• Implement conditional routing, retries, and fallback mechanisms to ensure robust execution.
• Integrate retrieval processes, APIs, and structured outputs into agent loops.
• Evaluate, monitor, and harden agent behavior to guarantee reliability and safety.

Course Format

• Interactive lectures and facilitated discussions.
• Guided labs and code walkthroughs conducted in a sandbox environment.
• Scenario-based design exercises accompanied by peer reviews.

Course Customization Options

• For those interested in a customized training session for this course, please reach out to us to arrange it.

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

This instructor-led, live training (online or onsite) 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 Customization Options

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