Prompt Engineering for Healthcare Training Course

Vertex AI offers sophisticated tools for fine-tuning large language models and managing prompts, empowering developers and data teams to enhance model accuracy, streamline iteration processes, and ensure rigorous evaluation through integrated 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 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.
• Establish prompt management workflows, including versioning and testing.
• Utilize evaluation libraries to benchmark and enhance AI performance.
• Deploy and monitor optimized models in production environments.

Course Format

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

Customization Options

• To request a customized version of this course, please contact us to make arrangements.

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

This guided, live training session—available either online or in-person—is designed for mid-level healthcare and data professionals looking to design, assess, and oversee agentic AI solutions for both clinical and operational scenarios.

Upon completing this course, participants will be equipped to:

• Clarify the 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 leveraging clinical records and knowledge repositories.
• Assess, monitor, and manage agent conduct using guardrails and human-supervised controls.

Training Format

• Engaging lectures complemented by guided discussions.
• Supervised hands-on labs and code demonstrations within a sandbox setting.
• Practical exercises focusing on safety, evaluation metrics, and governance frameworks.

Customization Options

• For tailored training solutions, please reach out to us to discuss your specific needs.

This instructor-led, live training in Turkey (online or on-site) 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:

• 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 instructor-led, live training in Turkey (online or onsite) is designed for intermediate-level healthcare professionals who aim to implement AI and AR/VR solutions for medical education, surgical simulations, and rehabilitation programs.

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

• Grasping how AI enhances AR/VR experiences within the healthcare domain.
• Utilizing AR/VR for surgical simulations and medical education.
• Implementing AR/VR tools for patient rehabilitation and therapy.
• Investigating the ethical and privacy issues associated with AI-enhanced medical instruments.

This instructor-led, live training in Turkey (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 live, instructor-led training in Turkey (online or on-site) is tailored for intermediate-level healthcare professionals and data scientists aiming to understand and deploy AI technologies in healthcare environments.

Upon completing this training, participants will be able to:

• Recognize primary healthcare challenges that AI solutions can resolve.
• Evaluate the influence of AI on patient safety, care quality, and medical research.
• Grasp the connection between AI technologies and healthcare business strategies.
• Apply core AI principles to real-world healthcare situations.
• Construct machine learning models for the analysis of medical data.

This instructor-led, live training in Turkey (online or onsite) is designed for healthcare professionals and researchers who want 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 Turkey (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 instructor-led, live training in Turkey (online or on-site) targets intermediate to advanced medical AI developers and data scientists who want to fine-tune models for clinical diagnosis, disease prediction, and patient outcome forecasting using structured and unstructured medical data.

Upon completing this training, participants will be able to:

• Fine-tune AI models using healthcare datasets such as electronic medical records (EMRs), imaging data, and time-series information.
• Implement transfer learning, domain adaptation, and model compression strategies within medical applications.
• Tackle issues related to privacy, algorithmic bias, and regulatory compliance during model development.
• Deploy and oversee fine-tuned models in practical healthcare settings.

Generative AI refers to technology capable of producing new content, such as text, images, and recommendations, by leveraging prompts and underlying data.

This instructor-led live training, available both online and onsite, is designed for healthcare professionals at beginner to intermediate levels who aim to utilize generative AI and prompt engineering to enhance efficiency, precision, and communication within medical environments.

Upon completing this training, participants will be able to:

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

Course Format

• Engaging lectures paired with interactive discussions.
• Practical exercises and real-world case studies.
• Hands-on experimentation with various AI tools.

Customization Options

• To request a tailored version of this training, please contact us to make arrangements.

This instructor-led live training in Turkey (online or onsite) is designed for beginner 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 stateful, multi-agent 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 clinical workflows.

This instructor-led live training, available online or on-site, is designed for intermediate to advanced professionals seeking to design, implement, and manage LangGraph-based healthcare solutions while navigating regulatory, ethical, and operational challenges.

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

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

Course Format

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

Customization Options

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

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

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

• Grasp the significance of multimodal AI in contemporary healthcare.
• Combine structured and unstructured medical data to support AI-driven diagnostics.
• Utilize AI techniques to evaluate medical images and electronic health records.
• Build predictive models for disease diagnosis and therapeutic recommendations.
• Deploy speech and natural language processing (NLP) tools for medical transcription and patient engagement.

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 professionals and IT teams seeking to deploy, customize, and operationalize Ollama-based AI solutions within clinical and administrative settings.

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

• Install and configure Ollama to ensure secure usage in healthcare environments.
• Integrate local Large Language Models (LLMs) into clinical workflows and administrative processes.
• Customize models to handle 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.

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-level practitioners seeking to implement TinyML solutions for healthcare monitoring and diagnostic applications.

Upon completion of this course, 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 for low-power and memory-constrained wearable devices.
• Assess the clinical relevance, reliability, and safety of TinyML-driven outputs.

Course Format

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

Customization Options

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