Generative AI and Prompt Engineering in Healthcare Training Course

Vertex AI provides advanced tools for fine-tuning large models and managing prompts, enabling developers and data teams to enhance model accuracy, streamline iteration workflows, and ensure rigorous evaluation with built-in libraries and services. This instructor-led, live training (online or onsite) is designed for intermediate to advanced practitioners who aim to improve the performance and reliability of generative AI applications using supervised fine-tuning, prompt versioning, and evaluation services in Vertex AI for government. By the end 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. - Utilize evaluation libraries to benchmark and optimize AI performance. - Deploy and monitor enhanced models in production environments. **Format of the Course** - Interactive lecture and discussion. - Hands-on labs with Vertex AI fine-tuning and prompt tools. - Case studies of enterprise model optimization for government. **Course Customization Options** - To request a customized training for this course, please contact us to arrange.

Agentic AI is an approach where artificial intelligence systems are designed to plan, reason, and utilize tools to achieve specific goals within established parameters. This instructor-led, live training (available online or on-site) is targeted at intermediate-level healthcare and data teams who aim to design, evaluate, and govern agentic AI solutions for both clinical and operational applications. The training aligns with the needs of public sector workflows, governance, and accountability for government agencies. By the end of this training, participants will be able to: - Explain the principles and constraints of agentic AI in healthcare contexts. - Design safe agent workflows that incorporate planning, memory, and tool usage. - Develop retrieval-augmented agents using clinical documents and knowledge bases. - Evaluate, monitor, and govern agent behavior through the use of guardrails and human-in-the-loop controls. **Format of the Course** - Interactive lectures and facilitated discussions. - Guided labs and code walkthroughs in a secure sandbox environment. - Scenario-based exercises focused on safety, evaluation, and governance. **Course Customization Options** - To request a customized training for government agencies or other organizations, please contact us to arrange.

This instructor-led, live training (online or onsite) is designed for intermediate to advanced healthcare professionals and artificial intelligence developers who seek to implement AI-driven healthcare solutions for government and private sector applications.

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

• Understand the role of AI agents in enhancing healthcare and diagnostics.
• Develop AI models for medical image analysis and predictive diagnostics.
• Integrate AI technologies with electronic health records (EHR) and clinical workflows.
• Ensure compliance with healthcare regulations and uphold ethical AI practices.

This instructor-led, live training (online or onsite) is designed for intermediate-level healthcare professionals who aim to integrate artificial intelligence (AI) and augmented reality/virtual reality (AR/VR) solutions into medical training, surgery simulations, and rehabilitation programs. By the end of this training, participants will be able to: - Comprehend the role of AI in enhancing AR/VR experiences within the healthcare sector. - Utilize AR/VR technologies for conducting surgery simulations and medical training. - Implement AR/VR tools in patient rehabilitation and therapeutic interventions. - Examine the ethical and privacy considerations associated with AI-enhanced medical applications, ensuring compliance with standards for government and regulatory requirements.

This instructor-led, live training (conducted online or on-site) is designed for intermediate-level data scientists and healthcare professionals who aim to utilize artificial intelligence 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. - Apply AI for predictive modeling in healthcare data. - Analyze medical images with AI-driven techniques. - Examine ethical considerations in AI-based healthcare solutions for government.

This instructor-led, live training (available online or onsite) is designed for intermediate-level healthcare professionals and data scientists who aim to understand and implement artificial intelligence (AI) technologies in healthcare settings. By the end of this training, participants will be able to: - Identify key challenges in healthcare that can be addressed through AI. - Analyze the impact of AI on patient care, safety, and medical research. - Understand the relationship between AI and healthcare business models. - Apply fundamental AI concepts to various healthcare scenarios. - Develop machine learning models for the analysis of medical data, enhancing decision-making processes for government and private sector applications.

This instructor-led, live training in [location] (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 training aligns with public sector workflows, governance, and accountability standards, ensuring that participants are equipped with the necessary skills to implement these technologies effectively for government and healthcare settings.

This instructor-led, live training (available online or onsite) is designed for intermediate-level healthcare professionals, biomedical engineers, and artificial intelligence developers who aim to utilize 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, ensuring alignment with public sector workflows, governance, and accountability for government.

This instructor-led, live training (online or onsite) is designed for intermediate to advanced medical AI developers and data scientists who aim to refine 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 electronic medical records (EMRs), imaging, and time-series data.
• Apply techniques such as transfer learning, domain adaptation, and model compression in medical contexts.
• Address privacy concerns, bias mitigation, and regulatory compliance in the development of AI models for government and healthcare settings.
• Deploy and monitor fine-tuned models in real-world healthcare environments to ensure effective and ethical use.

This instructor-led, live training in US (online or onsite) is designed for government and beginner 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 settings.
• Identify opportunities for leveraging generative AI to enhance drug discovery and personalized medicine.
• Utilize generative AI techniques for medical imaging and diagnostics to improve patient outcomes.
• Evaluate the ethical implications of AI technologies in medical environments.
• Develop strategies for integrating AI technologies into healthcare systems, ensuring alignment with public sector workflows, governance, and accountability.

LangGraph enables stateful, multi-actor workflows powered by LLMs with precise control over execution paths and state persistence. In healthcare, these capabilities are essential for compliance, interoperability, and building decision-support systems that align with medical workflows. This instructor-led, live training (online or onsite) is aimed at intermediate to advanced professionals who wish to design, implement, and manage LangGraph-based healthcare solutions while addressing regulatory, ethical, and operational challenges for government and private sector applications. By the end of this training, participants will be able to: - Design healthcare-specific LangGraph workflows with compliance and auditability in mind. - Integrate LangGraph applications with medical ontologies and standards (FHIR, SNOMED CT, ICD). - Apply best practices for reliability, traceability, and explainability in sensitive environments. - Deploy, monitor, and validate LangGraph applications in 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 [location] (online or onsite) is designed for intermediate to advanced healthcare professionals, medical researchers, and AI developers who seek to apply multimodal AI in medical diagnostics and healthcare applications for government. 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 to enhance AI-driven diagnostics. - Utilize AI techniques to analyze medical images and electronic health records. - Construct predictive models for disease diagnosis and treatment recommendations. - Implement speech and natural language processing (NLP) technologies for medical transcription and patient interaction.

Ollama is a lightweight platform designed for the local deployment of large language models.

This instructor-led, live training (available online or onsite) is tailored for intermediate-level healthcare practitioners and IT teams who seek to deploy, customize, and operationalize Ollama-based AI solutions within clinical and administrative environments.

Upon completing this training, participants will be able to:

• Install and configure Ollama for secure use in healthcare settings.
• Integrate local language models into clinical workflows and administrative processes.
• Customize models to align with healthcare-specific terminology and tasks.
• Implement best practices for privacy, security, and regulatory compliance.

Format of the Course

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

Course Customization Options

• To request a customized training for government or other specific needs, please contact us to arrange.

This instructor-led, live training (available online or onsite) is designed for intermediate-level healthcare professionals and artificial intelligence developers who aim to utilize prompt engineering techniques to enhance medical workflows, research efficiency, and patient outcomes.

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

• Comprehend the foundational principles of prompt engineering in the healthcare sector.
• Apply AI prompts for clinical documentation and patient interactions.
• Utilize AI tools for medical research and literature review.
• Improve drug discovery and clinical decision-making through AI-driven prompts.
• Ensure adherence to regulatory and ethical standards in healthcare AI applications, aligning with best practices for government and public sector workflows.

TinyML is the integration of machine learning into low-power, resource-limited wearable and medical devices for government and public sector applications. This instructor-led, live training (online or onsite) is aimed at intermediate-level practitioners who wish to implement TinyML solutions for healthcare monitoring and diagnostic applications. After 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 for AI-driven insights. - Optimize models for low-power and memory-constrained wearable devices. - Evaluate the clinical relevance, reliability, and safety of TinyML-driven outputs. **Format of the Course** - Lectures supported by live demonstrations and interactive discussion. - Hands-on practice with wearable device data and TinyML frameworks. - Implementation exercises in a guided lab environment. **Course Customization Options** - For tailored training that aligns with specific healthcare devices or regulatory workflows for government, please contact us to customize the program.