Ollama Applications in Healthcare Training Course

Advanced Ollama Model Debugging & Evaluation is an in-depth course focused on diagnosing, testing, and measuring model behavior when running local or private Ollama deployments.

This instructor-led, live training (online or onsite) is aimed at advanced-level AI engineers, ML Ops professionals, and QA practitioners who wish to ensure reliability, fidelity, and operational readiness of Ollama-based models in production.

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

• Perform systematic debugging of Ollama-hosted models and reproduce failure modes reliably.
• Design and execute robust evaluation pipelines with quantitative and qualitative metrics.
• Implement observability (logs, traces, metrics) to monitor model health and drift.
• Automate testing, validation, and regression checks integrated into CI/CD pipelines.

Format of the Course

• Interactive lecture and discussion.
• Hands-on labs and debugging exercises using Ollama deployments.
• Case studies, group troubleshooting sessions, and automation workshops.

Course Customization Options

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

Agentic AI refers to a methodology where AI systems autonomously plan, reason, and utilize tools to achieve objectives within specified limitations.

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

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

• Articulate the concepts and constraints of agentic AI within healthcare contexts.
• Design safe agent workflows that incorporate planning, memory, and tool usage.
• Construct retrieval-augmented agents leveraging clinical documents and knowledge bases.
• Evaluate, monitor, and govern agent behavior using guardrails and human-in-the-loop controls.

Format of the Course

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

Course Customization Options

• For customized training requests regarding this course, please contact us to arrange.

This instructor-led, live training in South Korea (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:

• 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 live, instructor-led training in South Korea (online or on-site) is tailored for intermediate-level healthcare professionals seeking to apply AI and AR/VR solutions for medical training, surgical simulations, and rehabilitation.

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

• Comprehend how AI enhances AR/VR experiences within the healthcare sector.
• Utilize AR/VR technologies for surgical simulations and medical training.
• Implement AR/VR tools effectively in patient rehabilitation and therapy.
• Examine the ethical and privacy issues associated with AI-enhanced medical devices.

This instructor-led live training in South Korea (online or onsite) is tailored for intermediate-level data scientists and healthcare professionals who intend to leverage AI for advanced healthcare applications via Google Colab.

By the conclusion 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 South Korea (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 South Korea (online or onsite) targets 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:

• Grasp the fundamentals of ChatGPT and its applications in healthcare.
• Use ChatGPT to automate healthcare processes and patient interactions.
• Deliver accurate medical information and support to patients through ChatGPT.
• Apply ChatGPT for medical research and data analysis.

This instructor-led, live training in South Korea (online or onsite) is designed for intermediate-level healthcare professionals, biomedical engineers, and AI developers who want to leverage Edge AI for innovative healthcare solutions.

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

• Grasp the role and advantages 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 South Korea (online or on-site) is designed for intermediate to advanced medical AI developers and data scientists who aim to adapt models for clinical diagnosis, disease prediction, and patient outcome forecasting using both structured and unstructured medical data.

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

• Fine-tune AI models using healthcare datasets including EMRs, imaging, and time-series data.
• Utilize transfer learning, domain adaptation, and model compression techniques within medical applications.
• Manage privacy concerns, bias issues, and regulatory compliance during model development.
• Deploy and monitor fine-tuned models effectively 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:

• Grasp the core concepts of generative AI and prompt engineering.
• Utilize 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 South Korea (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 empowers stateful, multi-agent workflows driven by LLMs, offering precise control over execution paths and state persistence. In the healthcare sector, these capabilities are essential for ensuring compliance, interoperability, and the development of decision-support systems that seamlessly integrate with medical workflows.

This instructor-led live training (available online or onsite) targets intermediate to advanced professionals who aim 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 as core principles.
• 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.

Course Format

• Interactive lectures and discussions.
• Hands-on exercises featuring real-world case studies.
• Implementation practice within 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 South Korea (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.

This instructor-led, live training in South Korea (online or onsite) is designed for intermediate-level healthcare professionals and AI developers seeking to apply prompt engineering techniques to improve medical workflows, research efficiency, and patient outcomes.

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

• Grasp the fundamentals of prompt engineering in healthcare.
• Utilize AI prompts for clinical documentation and patient interactions.
• Leverage AI for medical research and literature review.
• Enhance drug discovery and clinical decision-making with AI-driven prompts.
• Ensure compliance with regulatory and ethical standards in healthcare AI.

TinyML integrates machine learning into low-power, resource-constrained wearable and medical devices.

This instructor-led, live training (available online or onsite) targets intermediate-level practitioners looking 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 gain 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 supplemented by live demonstrations and interactive discussions.
• Hands-on practice utilizing wearable device data and TinyML frameworks.
• Implementation exercises conducted in a guided lab environment.

Customization Options

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