Prompt Engineering for Healthcare Training Course

Vertex AI offers sophisticated tools designed 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) is targeted at intermediate to advanced practitioners looking to improve the performance and reliability of generative AI applications using 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 versioning and testing.
• Leverage evaluation libraries to benchmark and optimize AI performance.
• Deploy and monitor improved models in production environments.

Format of the Course

• Interactive lecture and discussion.
• Hands-on labs featuring Vertex AI fine-tuning and prompt tools.
• Case studies focused on enterprise model optimization.

Course Customization Options

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

Agentic AI represents a paradigm where artificial intelligence systems engage in planning, reasoning, and executing tool-based actions to achieve specific objectives within set boundaries.

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

Upon completing this training, participants will be equipped to:

• Articulate the principles and constraints of agentic AI within healthcare environments.
• Architect secure agent workflows incorporating planning, memory, and tool utilization.
• Develop retrieval-augmented agents capable of processing clinical documents and knowledge bases.
• Assess, monitor, and regulate agent behavior through guardrails and human-in-the-loop oversight mechanisms.

Course Format

• Interactive lectures coupled with facilitated discussions.
• Guided laboratory exercises and code walkthroughs within a sandbox environment.
• Scenario-based activities focusing on safety, evaluation, and governance.

Customization Options

• For tailored training requests, please reach out to us to arrange.

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

Upon completing this training, participants will be capable of:

• Grasping the role of AI agents in healthcare and diagnostics.
• Creating AI models for medical image analysis and predictive diagnostics.
• Integrating AI with electronic health records (EHR) and clinical workflows.
• Ensuring compliance with healthcare regulations and ethical AI practices.

This instructor-led, live training in Italy (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 Italy (online or onsite) is designed for intermediate-level data scientists and healthcare professionals seeking to apply AI for advanced healthcare applications via 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 Italy (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 Italy (online or onsite) is designed for healthcare professionals and researchers who wish 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 Italy (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 guided, live training in Italy (online or in-person) targets medical AI developers and data scientists at the intermediate to advanced levels who wish to adapt 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:

• Adapting AI models to healthcare datasets, including EMRs, imaging data, and time-series information.
• Implementing transfer learning, domain adaptation, and model compression within medical applications.
• Navigating privacy concerns, bias mitigation, and regulatory compliance during model development.
• Deploying and overseeing fine-tuned models in practical healthcare settings.

Generative AI refers to technologies capable of creating new content, including text, images, and recommendations, by interpreting prompts and analyzing data.

This instructor-led live training, available online or on-site, is designed for healthcare professionals at beginner to intermediate levels who aim to leverage generative AI and prompt engineering to enhance efficiency, precision, and communication within medical settings.

Upon completing this training, participants will be able to:

• Grasp the core concepts of generative AI and prompt engineering.
• Utilize AI tools to streamline clinical, administrative, and research workflows.
• Ensure the ethical, secure, and compliant application of AI in healthcare.
• Refine prompts to achieve consistent and accurate outcomes.

Course Format

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

Customization Options

• For a customized version of this course, please contact us to arrange your requirements.

This instructor-led, live training in Italy (online or onsite) targets beginner to intermediate-level healthcare professionals, data analysts, and policymakers who wish to understand and apply generative AI within the healthcare sector.

By the conclusion 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 in clinical environments.
• Formulate strategies to integrate AI technologies into healthcare systems.

LangGraph facilitates stateful, multi-actor workflows driven by LLMs, offering precise control over execution paths and state persistence. In the healthcare sector, these features are essential for ensuring compliance, interoperability, and the development of decision-support systems that align with medical workflows.

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

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

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

Format of the Course

• Interactive lecture and discussion.
• Hands-on exercises with real-world case studies.
• Implementation practice in a live-lab environment.

Course Customization Options

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

This instructor-led, live training in Italy (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 serves as a lightweight platform designed for running large language models on local devices.

This instructor-led, live training session (available online or on-site) targets intermediate-level healthcare professionals and IT teams aiming to deploy, customize, and operationalize Ollama-based AI solutions within both clinical and administrative settings.

After 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 within a sandboxed healthcare simulation environment.

Course Customization Options

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

TinyML represents the fusion of machine learning capabilities with low-power, resource-constrained wearable and medical devices.

This instructor-led live training, available either online or onsite, is designed for intermediate-level professionals looking to deploy TinyML solutions for healthcare monitoring and diagnostic purposes.

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

• Designing and deploying TinyML models to process health data in real time.
• Collecting, preprocessing, and interpreting biosensor data to generate AI-driven insights.
• Optimizing models to function efficiently on wearable devices with limited power and memory.
• Assessing the clinical relevance, reliability, and safety of outputs generated by TinyML systems.

Course Format

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

Customization Options

• To arrange customized training that aligns with specific healthcare devices or regulatory workflows, please contact us.