Prompt Engineering for Healthcare Training Course

Vertex AI offers sophisticated tools for fine-tuning large models and managing prompts, empowering developers and data teams to enhance model accuracy, streamline iteration workflows, and ensure rigorous evaluation through built-in libraries and services.

This instructor-led, live training (available online or onsite) targets intermediate to advanced practitioners seeking to improve the performance and reliability of generative AI applications by leveraging supervised fine-tuning, prompt versioning, and evaluation services within Vertex AI.

Upon completing this training, participants will be able to:

• Apply supervised fine-tuning techniques to Gemini models in Vertex AI.
• Implement prompt management workflows that include versioning and testing.
• Leverage evaluation libraries to benchmark and optimize AI performance.
• Deploy and monitor enhanced models in production environments.

Format of the Course

• Interactive lectures and discussions.
• Hands-on labs utilizing Vertex AI fine-tuning and prompt tools.
• Case studies focused on enterprise model optimization.

Course Customization Options

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

Agentic AI represents an approach where AI systems autonomously plan, reason, and utilize tools to achieve specific objectives within established boundaries.

This instructor-led live training, available both online or onsite, is designed for intermediate-level healthcare and data professionals seeking to design, evaluate, and govern agentic AI solutions for clinical and operational scenarios.

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

• Articulating the core concepts and constraints of agentic AI within healthcare environments.
• Designing secure agent workflows that incorporate planning, memory, and tool integration.
• Developing retrieval-augmented agents tailored to clinical documentation and knowledge repositories.
• Evaluating, monitoring, and governing agent behavior using guardrails and human-in-the-loop controls.

Course Format

• Interactive lectures paired with facilitated discussions.
• Guided laboratory exercises and code walkthroughs conducted in a sandbox environment.
• Scenario-based practical applications focusing on safety, evaluation, and governance.

Customization Options

• For tailored training arrangements for this course, please reach out to us to coordinate your specific needs.

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

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

• Comprehend the role of AI agents in healthcare and diagnostics.
• Create 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 course in Vietnam (online or on-site) targets intermediate healthcare professionals aiming to leverage 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.
• Apply AR/VR for medical training and surgical simulations.
• Implement AR/VR tools for patient rehabilitation and therapy.
• Examine ethical and privacy challenges associated with AI-enhanced medical devices.

This instructor-led live training (available online or onsite) is designed for intermediate data scientists and healthcare professionals seeking to utilize AI for advanced medical applications via Google Colab.

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

• Build and implement AI models for healthcare using Google Colab.
• Apply AI techniques for predictive modeling in healthcare data.
• Analyze medical images using AI-driven methods.
• Investigate ethical issues related to AI-based healthcare solutions.

This instructor-led, live training in Vietnam (online or onsite) is designed for intermediate-level healthcare professionals and data scientists who want to learn how 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 Vietnam (online or onsite) targets healthcare professionals and researchers who wish to leverage ChatGPT to enhance patient care, streamline workflows, and improve healthcare outcomes.

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

• Grasp the fundamentals of ChatGPT and its applications within healthcare.
• Use ChatGPT to automate healthcare processes and patient interactions.
• Deliver precise medical information and support to patients via ChatGPT.
• Apply ChatGPT for medical research and data analysis.

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

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

• Grasp the role and advantages of Edge AI in the healthcare sector.
• Develop and deploy AI models on edge devices tailored for healthcare applications.
• Implement Edge AI solutions in wearable technology and diagnostic tools.
• Design and deploy patient monitoring systems utilizing Edge AI.
• Navigate ethical and regulatory considerations in healthcare AI applications.

This instructor-led, live training in Vietnam (online or onsite) is designed for intermediate to advanced medical AI developers and data scientists who aim to refine models for clinical diagnosis, disease prediction, and patient outcome forecasting using structured and unstructured medical data.

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

• Optimize AI models on healthcare datasets including EMRs, imaging, and time-series data.
• Apply transfer learning, domain adaptation, and model compression in medical contexts.
• Address privacy, bias, and regulatory compliance in 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 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.

This instructor-led, live training in Vietnam (online or onsite) is aimed at beginner-level to intermediate-level healthcare professionals, data analysts, and policy makers who wish to understand and apply generative AI in the context of healthcare.

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

• Explain the principles and applications of generative AI in healthcare.
• Identify opportunities for generative AI to enhance drug discovery and personalized medicine.
• Utilize generative AI techniques for medical imaging and diagnostics.
• Assess the ethical implications of AI in medical settings.
• Develop strategies for integrating AI technologies into healthcare systems.

LangGraph empowers the creation of stateful, multi-actor workflows driven by Large Language Models (LLMs), offering precise control over execution paths and state persistence. In the healthcare sector, these capabilities are essential for ensuring compliance, enabling interoperability, and developing decision-support systems that seamlessly align with medical processes.

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

Upon completing this training, participants will be able to:

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

Course Format

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

Customization Options

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

This instructor-led, live training in Vietnam (online or on-site) is designed for intermediate to advanced-level healthcare professionals, medical researchers, and AI developers who want 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 analyze 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 designed for running large language models locally.

This instructor-led, live training (available online or onsite) targets intermediate-level healthcare practitioners and IT teams who wish to deploy, customize, and operationalize Ollama-based AI solutions within clinical and administrative environments.

Upon completing this training, participants will be able to:

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

Format of the Course

• Interactive lecture and discussion.
• Hands-on demonstrations and guided exercises.
• Practical implementation in a sandboxed healthcare simulation environment.

Course Customization Options

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

TinyML involves embedding machine learning capabilities into low-power, resource-constrained wearable and medical devices.

This instructor-led live training (available online or onsite) is designed for intermediate-level professionals looking to implement TinyML solutions for health monitoring and diagnostic applications.

Upon completing this training, participants will be equipped to:

• Design and deploy TinyML models for real-time health data processing.
• Collect, preprocess, and interpret biosensor data to derive AI-driven insights.
• Optimize models to function efficiently on low-power and memory-limited wearable devices.
• Assess the clinical relevance, reliability, and safety of outputs generated by TinyML.

Course Format

• Lectures augmented with live demonstrations and interactive discussions.
• Practical exercises involving wearable device data and TinyML frameworks.
• Implementation tasks conducted in a guided lab environment.

Customization Options

• For customized training tailored to specific healthcare devices or regulatory workflows, please contact us to adapt the program.