Ollama Applications in Healthcare Training Course

The Advanced Ollama Model Debugging & Evaluation course provides a comprehensive deep dive into diagnosing, testing, and assessing model behavior within local or private Ollama deployments.

Delivered as live, instructor-led training available either online or on-site, this program is tailored for advanced AI engineers, MLOps professionals, and QA practitioners who aim to guarantee the reliability, accuracy, and operational readiness of Ollama-based models in production environments.

Upon completion of this training, participants will gain the ability to:

• Conduct systematic debugging of Ollama-hosted models and reliably reproduce failure scenarios.
• Design and implement robust evaluation pipelines incorporating both quantitative and qualitative metrics.
• Establish observability practices (including logs, traces, and metrics) to monitor model health and detect drift.
• Automate testing, validation, and regression checks integrated into CI/CD pipelines.

Course Format

• Interactive lectures and discussions.
• Practical labs and debugging exercises utilizing Ollama deployments.
• Case studies, group troubleshooting sessions, and automation workshops.

Customization Options

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

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

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

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

• Articulating the core concepts and constraints of agentic AI within healthcare environments.
• Constructing secure agent workflows that incorporate planning, memory mechanisms, and tool integration.
• Developing retrieval-augmented agents tailored to clinical documents and knowledge bases.
• Implementing evaluation, monitoring, and governance frameworks for agent behavior using guardrails and human-in-the-loop controls.

Course Format

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

Customization Options

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

This instructor-led, live training in Czech Republic (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.

Designed for intermediate-level healthcare professionals interested in leveraging AI and AR/VR for medical education, surgical simulations, and rehabilitation, this instructor-led live training is available either online or on-site in Czech Republic.

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

• Comprehend how AI enhances AR/VR experiences within healthcare.
• Utilize AR/VR for surgical simulations and medical education.
• Implement AR/VR tools for patient rehabilitation and therapeutic treatments.
• Examine the ethical and privacy challenges associated with AI-enhanced medical technologies.

This instructor-led live training, available online or on-site, is designed for intermediate-level data scientists and healthcare professionals seeking to harness AI for advanced healthcare applications using Google Colab.

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

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

This instructor-led, live training, available online or on-site, is designed for intermediate-level healthcare professionals and data scientists aiming to understand and apply AI technologies in healthcare environments.

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

• Recognize significant healthcare challenges that AI solutions can resolve.
• Evaluate the impact of AI on patient care, safety, and medical research.
• Comprehend the intersection of AI and healthcare business models.
• Apply core AI principles to real-world healthcare scenarios.
• Construct machine learning models for the analysis of medical data.

This instructor-led, live training in Czech Republic (online or onsite) is designed for healthcare professionals and researchers who aim to utilize ChatGPT to enhance patient care, optimize workflows, and improve healthcare outcomes.

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

• Comprehend the fundamentals of ChatGPT and its applications in healthcare.
• Leverage 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 Czech Republic (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 Czech Republic (online or onsite) targets 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.

Upon completion 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 techniques in medical contexts.
• Address privacy concerns, bias mitigation, and regulatory compliance during model development.
• Deploy and monitor fine-tuned models in real-world healthcare environments.

Generative AI refers to technologies capable of producing new content, including text, images, and tailored recommendations, derived from specific prompts and underlying data.

This instructor-led training session, available either online or on-site, is designed for healthcare professionals at beginner to intermediate levels. The course aims to equip participants with the skills to leverage generative AI and prompt engineering to enhance efficiency, accuracy, and communication within medical environments.

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 settings.
• Refine prompts to achieve reliable and precise outcomes.

Course Format

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

Customization Options

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

This instructor-led, live training in Czech Republic (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 facilitates stateful, multi-agent workflows driven by Large Language Models (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 seamlessly align with medical workflows.

This instructor-led training, available both online and on-site, targets intermediate to advanced professionals seeking to design, implement, and manage healthcare solutions using LangGraph, while addressing regulatory, ethical, and operational challenges.

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

• Design healthcare-specific LangGraph workflows that prioritize compliance and auditability.
• Integrate LangGraph applications with medical ontologies and standards, including 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.

Course Format

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

Customization Options

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

This instructor-led live training in Czech Republic (available online or on-site) is intended for intermediate to advanced-level healthcare professionals, medical researchers, and AI developers seeking 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 Czech Republic (online or onsite) is aimed at intermediate-level healthcare professionals and AI developers who wish to leverage prompt engineering techniques for improving medical workflows, research efficiency, and patient outcomes.

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

• Understand the fundamentals of prompt engineering in healthcare.
• Use 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 involves integrating machine learning into low-power, resource-constrained wearable and medical devices.

This instructor-led live training (available online or onsite) targets intermediate-level practitioners aiming to implement TinyML solutions for healthcare monitoring and diagnostic applications.

Upon completing this training, participants will be able to:

• Design and deploy TinyML models for real-time health data processing.
• Collect, preprocess, and interpret biosensor data to generate AI-driven insights.
• Optimize models for low-power and memory-limited wearable devices.
• Evaluate the clinical relevance, reliability, and safety of TinyML-driven outputs.

Course Format

• Lectures supported by live demonstrations and interactive discussion.
• Hands-on practice with wearable device data and TinyML frameworks.
• Implementation exercises conducted in a guided lab environment.

Course Customization Options

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