Prompt Engineering for Healthcare Training Course

Vertex AI equips developers and data teams with sophisticated tools for fine-tuning large language models and managing prompts. These capabilities allow teams to enhance model accuracy, streamline iterative workflows, and maintain rigorous evaluation standards through integrated libraries and services.

This instructor-led live training, available either online or onsite, is designed for intermediate to advanced practitioners aiming to boost the performance and reliability of generative AI applications. The course covers supervised fine-tuning, prompt versioning, and evaluation services within the Vertex AI environment.

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

• Apply supervised fine-tuning techniques to Gemini models in Vertex AI.
• Implement effective 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.

Format of the Course

• Interactive lectures and discussions.
• Practical labs involving Vertex AI fine-tuning and prompt tools.
• Case studies focusing on enterprise model optimization.

Course Customization Options

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

Agentic AI represents a paradigm where artificial intelligence systems autonomously plan, reason, and utilize tools to achieve specific objectives within defined constraints.

This instructor-led live training, available either online or onsite, is designed for intermediate-level healthcare and data teams aiming to design, assess, and govern agentic AI solutions for both clinical and operational applications.

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

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

Course Format

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

Course Customization Options

• For those interested in a tailored version of this course, please reach out to us to arrange your custom training.

This instructor-led, live training in Portugal (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:

• 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 instructor-led, live training in Portugal (online or onsite) is aimed at intermediate-level healthcare professionals who wish to apply AI and AR/VR solutions for medical training, surgery simulations, and rehabilitation.

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

• Understand the role of AI in enhancing 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 instructor-led live training in Portugal (online or onsite) targets intermediate data scientists and healthcare professionals keen on leveraging AI for advanced medical applications via Google Colab.

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

• Deploy AI models for healthcare using Google Colab.
• Use AI for predictive modelling in healthcare data.
• Analyze medical images with AI-driven techniques.
• Explore ethical considerations in AI-based healthcare solutions.

This instructor-led live training in Portugal (online or onsite) is designed for intermediate-level healthcare professionals and data scientists who aim to understand and implement AI technologies in healthcare settings.

By the conclusion 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 Portugal (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:

• Comprehend 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 Portugal (online or onsite) is designed for healthcare professionals, biomedical engineers, and AI developers at an intermediate level who wish to utilise Edge AI for innovative healthcare solutions.

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

• Comprehend the role and advantages of Edge AI in healthcare.
• Create and deploy AI models on edge devices for healthcare applications.
• Implement Edge AI solutions within wearable devices and diagnostic tools.
• Design and deploy patient monitoring systems using Edge AI.
• Tackle ethical and regulatory issues pertaining to healthcare AI applications.

This instructor-led, live training in Portugal (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 forecasting patient outcomes using structured and unstructured medical data.

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

• Fine-tune AI models using healthcare datasets, including Electronic Medical Records (EMRs), imaging data, and time-series data.
• Implement 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 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:

• 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 Portugal (online or onsite) targets beginner to intermediate-level healthcare professionals, data analysts, and policymakers who aim to understand and apply generative AI in the healthcare sector.

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

• Articulate the core principles and practical applications of generative AI in healthcare.
• Recognize opportunities where generative AI can improve drug discovery and personalized medicine.
• Apply generative AI techniques to medical imaging and diagnostics.
• Evaluate the ethical implications of AI deployment in medical environments.
• Formulate strategies for integrating AI technologies into existing healthcare systems.

LangGraph facilitates 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 vital for ensuring compliance, interoperability, and the development of decision-support systems that align with clinical workflows.

This instructor-led, live training (available online or onsite) targets intermediate to advanced 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 prioritise 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.

Format of the Course

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

Course Customization Options

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

This instructor-led, live training in Portugal (online or onsite) is aimed at intermediate-level to advanced-level healthcare professionals, medical researchers, and AI developers who wish 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) is designed for intermediate-level healthcare practitioners and IT teams looking to deploy, customize, and operationalize Ollama-based AI solutions within clinical and administrative settings.

Upon completing this training, participants will be able to:

• Install and configure Ollama for secure use in healthcare environments.
• 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.

Course Format

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

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 live training, available both online and onsite, is designed for intermediate-level professionals aiming to implement TinyML solutions for healthcare monitoring and diagnostic applications.

Upon completing this training, participants will be capable of:

• Designing and deploying TinyML models for real-time health data processing.
• Collecting, preprocessing, and interpreting biosensor data to generate AI-driven insights.
• Optimising models for wearable devices with limited power and memory.
• Assessing the clinical relevance, reliability, and safety of TinyML-generated outputs.

Course Format

• Lectures complemented by live demonstrations and interactive discussions.
• Practical exercises using wearable device data and TinyML frameworks.
• Guided implementation exercises within a lab environment.

Course Customization Options

• For bespoke training aligned with specific healthcare devices or regulatory workflows, please contact us to tailor the program.