Ollama Applications in Healthcare Training Course

Advanced Ollama Model Debugging & Evaluation is a comprehensive course dedicated to diagnosing, testing, and assessing model behavior in local or private Ollama deployments.

Delivered as instructor-led live training (available online or onsite), this program targets experienced AI engineers, MLOps professionals, and QA specialists who aim to ensure the reliability, accuracy, and operational readiness of Ollama-based models in production environments.

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

• Systematically debug Ollama-hosted models and reliably reproduce failure scenarios.
• Design and execute robust evaluation pipelines using both quantitative and qualitative metrics.
• Implement observability measures (logs, traces, metrics) to monitor model health and detect drift.
• Automate testing, validation, and regression checks within CI/CD pipelines.

Course Format

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

Course Customization Options

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

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

This instructor-led, live training (available online or onsite) is designed for intermediate-level healthcare and data teams interested in creating, evaluating, and governing agentic AI solutions for clinical and operational scenarios.

Upon completing this training, participants will be equipped to:

• Articulate the core concepts and limitations of agentic AI within healthcare environments.
• Develop secure agent workflows incorporating planning, memory management, and tool integration.
• Construct retrieval-augmented agents utilizing clinical documents and knowledge bases.
• Assess, monitor, and govern agent behavior using guardrails and human-in-the-loop controls.

Format of the Course

• Interactive lectures complemented by guided discussions.
• Supervised labs and code walkthroughs conducted in a sandbox environment.
• Scenario-based exercises focused on safety, evaluation, and governance.

Course Customization Options

• For customized training arrangements for this course, please contact us.

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

Upon completing this training, participants will be able to:

• Grasp the role of AI agents in healthcare and diagnostics.
• Create 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 Bulgaria (online or onsite) is designed for intermediate-level healthcare professionals aiming to apply AI and AR/VR solutions for medical training, surgery simulations, and rehabilitation.

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

• Grasp how AI enhances AR/VR experiences within the healthcare sector.
• Utilize AR/VR for conducting surgery simulations and professional medical training.
• Implement AR/VR tools effectively in patient rehabilitation and therapy.
• Investigate the ethical and privacy challenges associated with AI-enhanced medical instruments.

This instructor-led live session, conducted online or on-site, targets intermediate-level data scientists and healthcare experts aiming to utilize AI for sophisticated healthcare applications through Google Colab.

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

• Develop healthcare-specific AI models using Google Colab.
• Leverage AI for predictive modeling in healthcare data.
• Analyze medical images using AI-driven techniques.
• Examine ethical issues inherent to AI-based healthcare solutions.

This instructor-led, live training in Bulgaria (online or onsite) is designed for intermediate-level healthcare professionals and data scientists eager to comprehend and implement AI technologies within healthcare settings.

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

• Recognizing primary healthcare challenges that AI can resolve.
• Assessing the influence of AI on patient safety, care quality, and medical research.
• Grasping the interplay between AI and healthcare business strategies.
• Applying core AI principles to real-world healthcare scenarios.
• Constructing machine learning models for the analysis of medical data.

This instructor-led live training in Bulgaria (online or onsite) is intended for healthcare professionals and researchers who aim to leverage ChatGPT to improve patient care, streamline workflows, and enhance 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 Bulgaria (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 Bulgaria (online or in-person) targets intermediate to advanced medical AI developers and data scientists aiming to refine 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:

• Refining AI models on healthcare datasets, including EMRs, imaging, and time-series data.
• Implementing transfer learning, domain adaptation, and model compression within medical contexts.
• Tackling issues of privacy, bias, and regulatory compliance during model development.
• Deploying and monitoring refined models 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:

• 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 Bulgaria (online or onsite) targets beginner to intermediate healthcare professionals, data analysts, and policymakers interested in understanding and applying generative AI in 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-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 seamlessly align with clinical workflows.

This instructor-led live training (available online or onsite) targets intermediate to advanced professionals aiming 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 key considerations.
• 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.

Course Customization Options

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

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

By the conclusion 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 session Bulgaria (online or on-site) is designed for intermediate-level healthcare professionals and AI developers who aim to utilize prompt engineering techniques to optimize medical workflows, increase research efficiency, and improve patient outcomes.

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

• Grasp the core principles of prompt engineering within the healthcare context.
• Apply AI prompts for clinical documentation and patient communication.
• Utilize AI tools for medical research and literature reviews.
• Improve drug discovery and clinical decision-making through AI-powered prompts.
• Maintain compliance with regulatory and ethical standards in healthcare AI applications.

TinyML involves embedding machine learning capabilities into low-power, resource-constrained wearable and medical devices.

This instructor-led training, available online or onsite, targets intermediate-level practitioners looking to implement TinyML solutions for healthcare monitoring and diagnostic purposes.

Upon completing this training, participants will be equipped to:

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

Course Format

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

Customization Options

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