Prompt Engineering for Healthcare Training Course

Vertex AI offers sophisticated tools for fine-tuning large language models and managing prompts, allowing developers and data teams to enhance model accuracy, streamline iterative workflows, and ensure rigorous evaluation through integrated libraries and services.

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

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

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

Course Format

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

Customization Options

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

Agentic AI refers to a methodology where artificial intelligence systems are capable of planning, reasoning, and utilizing tools to achieve specific objectives within established boundaries.

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

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

• Articulate the core principles and limitations of agentic AI within healthcare environments.
• Construct secure agent workflows that incorporate planning, memory retention, and tool integration.
• Develop retrieval-augmented agents that leverage clinical documentation and knowledge repositories.
• Assess, oversee, and govern agent conduct using safety guardrails and human-in-the-loop mechanisms.

Course Format

• Interactive lectures coupled with guided discussions.
• Directed laboratory exercises and code walkthroughs conducted in a sandbox setting.
• Scenario-based activities focusing on safety, assessment, and governance.

Customization Options

• To arrange a tailored training program for this course, please reach out to us.

This instructor-led, live training in France (online or onsite) is designed for healthcare professionals and AI developers at intermediate to advanced levels 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 guided, live training in France (online or on-site) is designed for intermediate-level healthcare experts looking to utilize AI and AR/VR solutions for medical education, surgical simulations, and rehabilitation processes.

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

• Grasping how AI improves AR/VR experiences within healthcare.
• Utilizing AR/VR for surgical simulations and medical education.
• Implementing AR/VR tools in patient rehabilitation and therapy.
• Examining the ethical and privacy challenges posed by AI-enhanced medical technologies.

This instructor-led, live training conducted in France (either online or on-site) is directed at intermediate-level data scientists and healthcare professionals eager to harness AI for sophisticated healthcare applications using Google Colab.

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

• Implement healthcare-specific AI models using Google Colab.
• Utilize AI for predictive modeling within healthcare data.
• Analyze medical images employing AI-driven techniques.
• Investigate ethical considerations inherent in AI-based healthcare solutions.

This instructor-led, live training in France (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 France (online or onsite) is designed for healthcare professionals and researchers who aim to harness ChatGPT to improve patient care, optimize workflows, and achieve better healthcare outcomes.

Upon completing this training, participants will be equipped to:

• Grasp the fundamental concepts of ChatGPT and its applications in healthcare.
• Employ ChatGPT to automate healthcare processes and manage interactions.
• Deliver precise medical information and support to patients using ChatGPT.
• Apply ChatGPT for medical research and analytical tasks.

This live, instructor-led training held in France (online or onsite) is designed for intermediate-level healthcare professionals, biomedical engineers, and AI developers seeking to harness 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 France (online or onsite) targets intermediate to advanced-level medical AI developers and data scientists who wish 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:

• Refine 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 refined 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:

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

Format of the Course also allows for the evaluation of participants.

• 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 France (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 facilitates stateful, multi-actor workflows driven by Large Language Models (LLMs), offering precise control over execution paths and state persistence. These capabilities are essential in healthcare for ensuring compliance, enabling interoperability, and developing decision-support systems that align with clinical workflows.

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

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

• Design LangGraph workflows tailored to healthcare, ensuring 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 in healthcare production settings.

Course Format

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

Customization Options

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

This instructor-led live training in France (online or onsite) is aimed at intermediate to advanced healthcare professionals, medical researchers, and AI developers looking 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 seeking to deploy, customize, and operationalize Ollama-based AI solutions within clinical and administrative environments.

Upon completion of 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 also allows for the evaluation of participants.

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

Course Customization Options

• To request 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 training session, available online or onsite, is designed for intermediate-level practitioners seeking to implement TinyML solutions for healthcare monitoring and diagnostic applications.

Upon completion, participants will be able 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 for low-power and memory-constrained wearable devices.
• Evaluate the clinical relevance, reliability, and safety of TinyML-driven outputs.

Course Format

• Lectures complemented by live demonstrations and interactive discussions.
• Hands-on practice with wearable device data and TinyML frameworks.
• Implementation exercises conducted in a guided lab environment.

Customization Options

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