Generative AI and Prompt Engineering in Healthcare Training Course

Vertex AI offers sophisticated tools for fine-tuning large language models and managing prompts, empowering developers and data teams to enhance model accuracy, streamline iteration workflows, and ensure rigorous evaluation through integrated libraries and services.

This instructor-led, live training (available online or onsite) is designed for intermediate to advanced practitioners seeking to improve the performance and reliability of generative AI applications using supervised fine-tuning, prompt versioning, and evaluation services within Vertex AI.

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

• Apply supervised fine-tuning techniques to Gemini models in Vertex AI.
• Implement prompt management workflows, including versioning and testing.
• Leverage evaluation libraries to benchmark and optimise AI performance.
• Deploy and monitor enhanced models in production environments.

Course Format

• Interactive lectures and discussions.
• Hands-on labs featuring Vertex AI fine-tuning and prompt tools.
• Case studies on enterprise model optimisation.

Course Customisation Options

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

Agentic AI represents a methodology where artificial intelligence systems formulate plans, perform reasoning, and execute actions using various tools to achieve specific objectives within set constraints.

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

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

• Articulating agentic AI concepts and constraints within healthcare environments.
• Developing safe agent workflows that incorporate planning, memory, and tool utilization.
• Constructing retrieval-augmented agents that operate over clinical documents and knowledge bases.
• Evaluating, monitoring, and governing agent behaviour through the use of guardrails and human-in-the-loop controls.

Course Format

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

Course Customization Options

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

This instructor-led, live training in Botswana (online or onsite) is designed for intermediate to advanced healthcare professionals and AI developers looking to implement AI-driven healthcare solutions.

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

• Comprehend 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 Botswana (online or onsite) 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 instructor-led, live training in Botswana (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 Botswana (online or onsite) is designed for 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 Botswana (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 Botswana (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 Botswana (online or onsite) targets intermediate to advanced 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.

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 within medical contexts.
• Address privacy, bias, and regulatory compliance issues in model development.
• Deploy and monitor fine-tuned models in real-world healthcare environments.

This instructor-led, live training in Botswana (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 personalised medicine.
• Utilise 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, achieving interoperability, and developing decision-support systems that align with clinical workflows.

This instructor-led training, available online or onsite, is designed for intermediate to advanced professionals seeking to design, implement, and manage LangGraph-based healthcare solutions while navigating regulatory, ethical, and operational challenges.

Upon completing this training, participants will be able to:

• Design healthcare-specific LangGraph workflows that prioritise 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 lectures and discussions.
• Hands-on exercises using real-world case studies.
• Implementation practice in a live laboratory environment.

Course Customization Options

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

This instructor-led, live training in Botswana (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.

Ollama is a slim platform designed for running large language models on your own devices.

This instructor-led, live training (available online or on-site) targets intermediate healthcare professionals and IT teams who want to deploy, tailor, and run Ollama-based AI solutions in clinical and administrative settings.

After finishing this training, participants will be able to:

• Set up Ollama for secure use in healthcare environments.
• Integrate local LLMs into clinical workflows and admin processes.
• Tailor models for healthcare-specific language and tasks.
• Apply best practices for privacy, security, and regulatory compliance.

Course Format

• Interactive lectures and discussions.
• Hands-on demonstrations and guided exercises.
• Practical implementation in a sandboxed healthcare simulation environment.

Customisation Options

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

This instructor-led, live training in Botswana (online or on-site) 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 online or onsite, is designed for intermediate-level practitioners aiming to implement TinyML solutions for healthcare monitoring and diagnostic purposes.

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 to suit low-power and memory-limited wearable devices.
• Evaluate the clinical relevance, reliability, and safety of TinyML-generated outputs.

Format of the Course

• Lectures accompanied 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.