Ollama Applications in Healthcare Training Course

Advanced Ollama Model Debugging & Evaluation is a comprehensive course designed to diagnose, test, and measure model behaviour when running local or private Ollama deployments.

This instructor-led, live training (available 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 are capable of planning, reasoning, and utilizing tools to achieve specific objectives within established boundaries.

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

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

• Articulate the concepts and constraints of agentic AI within healthcare environments.
• Design safe agent workflows incorporating planning, memory capabilities, and tool usage.
• Develop retrieval-augmented agents using clinical documents and knowledge bases.
• Evaluate, monitor, and govern agent behaviour through the implementation of guardrails and human-in-the-loop controls.

Course Format

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

Customization Options

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

This instructor-led, live training in Kenya, offered online or onsite, caters to intermediate to advanced healthcare professionals and AI developers seeking to deploy AI-driven healthcare solutions.

By the conclusion 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 Kenya (online or in-person) is aimed at intermediate-level healthcare professionals who wish to apply AI and AR/VR solutions for medical training, surgery simulations, and rehabilitation.

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

• Understand the role of AI in enhancing AR/VR experiences in healthcare.
• Use AR/VR for surgery simulations and medical training.
• Apply AR/VR tools in patient rehabilitation and therapy.
• Explore the ethical and privacy concerns in AI-enhanced medical tools.

This live, instructor-led session in Kenya (available online or onsite) is tailored for intermediate data scientists and healthcare experts aiming to apply AI for sophisticated medical applications via Google Colab.

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

• Deploy AI models for healthcare use cases using Google Colab.
• Apply AI for predictive modeling within healthcare data.
• Analyze medical imagery using AI-driven techniques.
• Examine ethical considerations inherent in AI-based healthcare solutions.

This instructor-led, live training in Kenya (online or onsite) is designed for intermediate-level healthcare professionals and data scientists who seek to understand and implement AI technologies within healthcare environments.

Upon completing this training, participants will be capable of:

• Recognizing primary healthcare challenges that AI can resolve.
• Evaluating AI’s influence on patient safety, care quality, and medical research.
• Grasping the interplay between AI and healthcare business frameworks.
• Applying core AI principles to real-world healthcare situations.
• Constructing machine learning models tailored for medical data analysis.

This instructor-led live training in Kenya (online or onsite) targets healthcare professionals and researchers seeking to utilize 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 Kenya (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 Kenya (online or onsite) is designed for medical AI developers and data scientists at intermediate to advanced levels who wish 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 EMRs, imaging, and time-series data.
• Implement transfer learning, domain adaptation, and model compression within medical contexts.
• Manage privacy concerns, bias, and regulatory compliance during model development.
• Deploy and monitor fine-tuned 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 Kenya (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-actor workflows driven by LLMs, offering precise control over execution paths and state persistence. In the healthcare sector, these capabilities are essential for ensuring compliance, enhancing interoperability, and developing decision-support systems that align with clinical workflows.

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

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

• Design healthcare-specific LangGraph workflows with a focus on 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.

Course Customization Options

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

This instructor-led, live training in Kenya (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 Kenya (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 embedding machine learning capabilities into low-power, resource-constrained wearable and medical devices.

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

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

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

Course Format

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

Course Customization Options

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