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) is designed for intermediate to advanced practitioners aiming to improve the performance and reliability of generative AI applications using supervised fine-tuning, prompt versioning, and evaluation services within Vertex AI.

By the end 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.
• 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 focusing on Vertex AI fine-tuning and prompt tools.
• Case studies on enterprise model optimization.

Course Customization Options

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

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

This instructor-led training session, available either online or at your premises, is tailored for intermediate-level healthcare and data professionals seeking to design, assess, and oversee agentic AI solutions for clinical and operational scenarios.

Upon completing this training, participants will be capable of:

• Articulating the core concepts and limitations of agentic AI within healthcare environments.
• Constructing secure agent workflows that incorporate planning, memory retention, and tool utilization.
• Developing retrieval-augmented agents capable of processing clinical documents and knowledge bases.
• Assessing, monitoring, and governing agent behavior through the implementation of safety guardrails and human-in-the-loop controls.

Training Format

• Engaging lectures coupled with guided discussions.
• Hands-on labs and code walkthroughs conducted in a sandbox environment.
• Scenario-based exercises focusing on safety protocols, evaluation techniques, and governance frameworks.

Customization Options

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

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

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

• Grasp the significance of AI agents within healthcare and diagnostic contexts.
• Create AI models tailored for medical image analysis and predictive diagnostics.
• Seamlessly integrate AI tools with electronic health records (EHR) and clinical workflows.
• Guarantee adherence to healthcare regulations and ethical AI standards.

This instructor-led, real-time training in Norway (online or in-person) is designed for intermediate-level healthcare professionals who wish to implement AI and AR/VR solutions for medical education, surgical simulations, and rehabilitation.

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

• Understand how AI enhances 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 Norway (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 Norway (online or onsite) is designed for 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 Norway (online or onsite) targets healthcare professionals and researchers eager to harness ChatGPT to improve patient care, optimize workflows, and enhance healthcare outcomes.

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

• Grasp the fundamentals of ChatGPT and its specific applications in healthcare.
• Deploy ChatGPT to automate healthcare processes and patient interactions.
• Deliver precise medical information and support to patients via ChatGPT.
• Apply ChatGPT for medical research and analytical tasks.

This instructor-led, live training in Norway (online or onsite) targets 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 Norway (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.

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

• Fine-tune 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 Norway (online or onsite) is designed for beginner to intermediate-level healthcare professionals, data analysts, and policymakers who wish to understand and apply generative AI within the healthcare context.

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

• Articulate the core principles and applications of generative AI in healthcare.
• Identify opportunities for generative AI to improve drug discovery and personalized medicine.
• Apply generative AI techniques to medical imaging and diagnostics.
• Evaluate the ethical implications of AI in medical environments.
• Formulate 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. In the healthcare sector, these capabilities are essential for ensuring compliance, enabling interoperability, and developing decision-support systems that integrate seamlessly with medical workflows.

This instructor-led, live training session (available online or onsite) targets intermediate to advanced professionals aiming 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 healthcare-specific LangGraph workflows with a focus on compliance and auditability.
• 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 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 customized training for this course, please contact us to arrange.

This instructor-led, live training in Norway (online or onsite) is designed for healthcare professionals, medical researchers, and AI developers at intermediate to advanced levels who want to apply multimodal AI in medical diagnostics and healthcare applications.

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

• Grasp 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 tailored for intermediate-level healthcare practitioners and IT teams looking to deploy, customize, and operationalize Ollama-based AI solutions within 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 large language models 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.

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 live training (available online or onsite) targets intermediate practitioners 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 derive AI-driven insights.
• Optimizing models for low-power and memory-constrained wearable devices.
• Assessing the clinical relevance, reliability, and safety of TinyML-generated outputs.

Course Format

• Lectures complemented by live demonstrations and interactive discussions.
• Practical exercises involving wearable device data and TinyML frameworks.
• Implementation tasks conducted within a guided laboratory environment.

Customization Options

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