LangGraph in Healthcare: Workflow Orchestration for Regulated Environments Training Course

LangGraph serves as a framework for constructing stateful, multi-actor LLM applications as composable graphs that maintain persistent state and provide granular control over execution.

This instructor-led live training, available online or onsite, is designed for advanced AI platform engineers, DevOps professionals specializing in AI, and ML architects who aim 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 to enhance speed, reduce costs, and ensure scalability.
• Engineer reliability by implementing retries, timeouts, idempotency, and checkpoint-based recovery mechanisms.
• Debug and trace graph executions, inspect state, and systematically reproduce production issues.
• Instrument graphs with logs, metrics, and traces, deploy them to production, and monitor SLAs and associated costs.

Format of the Course

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

Course Customization Options

• To request a customized training version of this course, please contact us to make arrangements.

Agentic AI represents a methodology where artificial intelligence systems autonomously plan, reason, and execute actions using various tools to achieve specific objectives within established boundaries.

This instructor-led training session, available both online and on-site, is designed for healthcare and data teams at an intermediate level. It focuses on helping participants design, assess, and govern agentic AI solutions tailored for clinical and operational scenarios.

Upon completing this training, participants will be equipped to:

• Articulate the core concepts and limitations of agentic AI within the healthcare sector.
• Create secure agent workflows incorporating planning, memory management, and tool integration.
• Develop retrieval-augmented agents capable of processing clinical documents and knowledge bases.
• Assess, monitor, and govern agent behavior through the implementation of guardrails and human-in-the-loop oversight.

Course Format

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

Course Customization Options

• For organizations seeking a customized version of this training, please reach out to us to arrange a tailored experience.

This instructor-led, live training in Argentina (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 live, instructor-led training in Argentina (online or in-person) is designed for intermediate healthcare professionals who aim to apply AI and AR/VR solutions for medical education, surgical simulations, and rehabilitation.

Upon completion of this training, participants will be capable of:

• Comprehending how AI improves AR/VR applications in healthcare.
• Leveraging AR/VR for surgical simulations and medical education.
• Utilizing AR/VR tools in patient rehabilitation and therapeutic contexts.
• Examining the ethical and privacy issues associated with AI-enhanced medical technologies.

This instructor-led, live training in Argentina (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 modeling in healthcare data.
• Analyze medical images with AI-driven techniques.
• Explore ethical considerations in AI-based healthcare solutions.

This instructor-led, live training in Argentina (online or on-site) is designed for intermediate-level healthcare professionals and data scientists who wish to comprehend and implement AI technologies in healthcare environments.

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

• Identify critical 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 Argentina (online or onsite) is designed for healthcare professionals and researchers who aim to leverage ChatGPT to enhance patient care, streamline workflows, and improve healthcare outcomes.

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

• Understand the fundamentals of ChatGPT and its applications in healthcare.
• Utilize 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 Argentina (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 Argentina (online or onsite) is designed for intermediate to advanced medical AI developers and data scientists who aim to fine-tune models for clinical diagnosis, disease prediction, and patient outcome forecasting using structured and unstructured medical data.

Upon completion of this training, participants will be capable of:

• Fine-tuning AI models on healthcare datasets, including EMRs, imaging, and time-series data.
• Implementing transfer learning, domain adaptation, and model compression techniques within medical contexts.
• Addressing privacy concerns, bias, and regulatory compliance during model development.
• Deploying and monitoring 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 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:

• 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.
• Optimize 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 Customization Options

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

LangGraph serves as a framework designed for constructing stateful, multi-agent Large Language Model applications through composable graphs that maintain persistent state and provide granular control over execution flows.

This instructor-led live training, available both online and on-site, targets intermediate to advanced professionals aiming to design, implement, and manage LangGraph-based solutions within the finance sector, ensuring robust governance, observability, and regulatory compliance.

Upon completion of this training, participants will be capable of:

• Developing finance-specific LangGraph workflows that align with regulatory standards and audit requirements.
• Integrating financial data standards and ontologies into graph states and associated tooling.
• Establishing reliability, safety mechanisms, and human-in-the-loop controls for critical operational processes.
• Deploying, monitoring, and optimizing LangGraph systems to enhance performance, manage costs, and meet Service Level Agreements (SLAs).

Course Format

• Interactive lectures and group discussions.
• Extensive exercises and practical applications.
• Hands-on implementation within a live laboratory environment.

Customization Options

• For customized training arrangements, please reach out to our team.

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 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.

Format of the Course

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

Course Customization Options

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

LangGraph serves as a framework designed for constructing stateful, multi-actor LLM applications through composable graphs that offer persistent state and precise control over execution.

This instructor-led, live training (available online or onsite) is targeted at intermediate to advanced professionals seeking to design, implement, and operate LangGraph-based legal solutions that meet necessary compliance, traceability, and governance standards.

Upon completion of this training, participants will be able to:

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

Course Format

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

Customization Options

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

LangGraph serves as a framework designed for composing graph-structured workflows involving Large Language Models (LLMs), enabling capabilities such as branching, tool utilization, memory management, and controlled execution.

This instructor-led live training, available both online and on-site, targets intermediate-level engineers and product teams aiming to integrate LangGraph’s graph logic with LLM agent loops. The goal is to create dynamic, context-aware applications, including customer support agents, decision trees, and information retrieval systems.

Upon completion of this training, participants will be equipped to:

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

Course Format

• Interactive lectures accompanied by facilitated discussions.
• Guided laboratory sessions and code walkthroughs within a sandbox environment.
• Scenario-based design exercises and peer review sessions.

Customization Options for the Course

• For requests regarding customized training for this course, please contact us to make arrangements.

LangGraph is a graph-based orchestration framework that enables conditional, multi-step LLM and tool workflows, ideal for automating and personalizing 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 personalization.
• Manage state, memory, and context across multi-step campaigns.
• Evaluate, monitor, and optimize 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 personalization, segmentation, and branching logic.

Course Customization Options

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