Ollama Applications in Healthcare Training Course

Advanced Ollama Model Debugging & Evaluation is an intensive course dedicated to diagnosing, testing, and measuring model behaviour within local or private Ollama deployments.

This instructor-led live training (delivered online or onsite) caters to advanced-level AI engineers, ML Ops professionals, and QA practitioners aiming to guarantee the reliability, fidelity, and operational readiness of Ollama-based models in production environments.

Upon completing this training, participants will be able to:

• Systematically debug Ollama-hosted models and reliably reproduce failure modes.
• Design and execute robust evaluation pipelines utilising both quantitative and qualitative metrics.
• Implement observability features (logs, traces, 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.
• Hands-on labs and debugging exercises using Ollama deployments.
• Case studies, group troubleshooting sessions, and automation workshops.

Customisation Options

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

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

This instructor-led training session, available either online or at your location, is designed for intermediate-level healthcare professionals and data teams looking to create, assess, and manage agentic AI solutions for both clinical and operational scenarios.

Upon completing this training, participants will be equipped to:

• Clarify the principles and limitations of agentic AI within healthcare settings.
• Construct secure agent workflows that incorporate planning, memory retention, and tool integration.
• Develop retrieval-augmented agents capable of processing clinical documents and knowledge repositories.
• Assess, supervise, and regulate agent activities using safety guardrails and human oversight mechanisms.

Course Format

• Interactive lectures paired with guided discussions.
• Hands-on labs and code walkthroughs conducted in a sandbox environment.
• Practical exercises focused on safety, evaluation, and governance scenarios.

Customization Options

• For tailored training arrangements, please get in touch with us.

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

By the end of 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 Malaysia (online or onsite) is designed for intermediate-level healthcare professionals looking to utilise AI and AR/VR solutions for medical education, surgical simulations, and rehabilitation.

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

• Comprehend how AI enhances AR/VR experiences in healthcare.
• Utilise AR/VR for surgical simulations and medical training.
• Deploy AR/VR tools for patient rehabilitation and therapy.
• Examine the ethical and privacy issues surrounding AI-enhanced medical instruments.

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

Upon completing this training, participants will be equipped to:

• Pinpoint significant healthcare challenges that AI can resolve.
• Evaluate the influence of AI on patient care, safety protocols, and medical research.
• Grasp the interplay between AI and healthcare business models.
• Apply core AI concepts to real-world healthcare scenarios.
• Construct machine learning models tailored for medical data analysis.

This instructor-led, live training in Malaysia (online or onsite) is designed for healthcare professionals and researchers who wish 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 Malaysia (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 Malaysia (online or onsite) is aimed at intermediate-level to advanced-level medical AI developers and data scientists who wish 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 Malaysia (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-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, interoperability, and the development of decision-support systems that seamlessly align with medical workflows.

This instructor-led live training session is available both online and onsite. It is designed for intermediate to advanced-level professionals aiming to design, implement, and manage LangGraph-based healthcare solutions while effectively addressing regulatory, ethical, and operational challenges.

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

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

Format of the Course

• Interactive lecture and discussion.
• Hands-on exercises with real-world case studies.
• Implementation practice in a live-lab environment.

Course Customization Options

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

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

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

• Comprehend the role of multimodal AI in modern healthcare.
• Integrate structured and unstructured medical data for AI-driven diagnostics.
• Apply AI techniques to analyse 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 Malaysia (online or onsite) is designed for intermediate-level healthcare professionals and AI developers who aim to leverage prompt engineering techniques to enhance 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 embedding machine learning capabilities into low-power, resource-constrained wearable and medical devices.

This instructor-led live training, available online or onsite, is designed for intermediate-level practitioners seeking 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 derive AI-driven insights.
• Optimize models for low-power and memory-constrained wearable devices.
• Evaluate the clinical relevance, reliability, and safety of outputs generated by TinyML.

Course Format

• Lectures complemented by live demonstrations and interactive discussions.
• Hands-on practice using 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.