Prompt Engineering for Healthcare Training Course

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

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

Upon completing this training, participants will be able to:

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

Course Format

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

Course Customization Options

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

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

This instructor-led training, available either online or on-site, is designed for intermediate-level healthcare and data professionals looking to design, assess, and govern agentic AI solutions for both clinical and operational scenarios.

Upon completion of this training, participants will gain the ability to:

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

Course Format

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

Course Customization Options

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

This instructor-led, live training in Mexico (online or on-site) targets intermediate to advanced healthcare professionals and AI developers who aim 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 Mexico (online or on-site) is designed for intermediate-level healthcare professionals who aim to leverage AI and AR/VR solutions for medical training, surgical simulations, and rehabilitation programs.

Upon completing this training, participants will be able to:

• Grasp how AI enhances AR/VR experiences within healthcare.
• Utilize AR/VR for surgical simulations and medical education.
• Implement AR/VR tools in patient rehabilitation and therapy.
• Examine the ethical and privacy issues associated with AI-enhanced medical devices.

This instructor-led, live training in Mexico (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 Mexico (online or onsite) is aimed at 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 Mexico (online or onsite) is aimed at 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:

• Grasp the fundamentals of ChatGPT and its applications in healthcare.
• Leverage ChatGPT to automate healthcare processes and interactions.
• Deliver accurate medical information and support to patients using ChatGPT.
• Apply ChatGPT for medical research and analysis.

This instructor-led, live training in Mexico (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 Mexico (online or on-site) is designed for medical AI developers and data scientists at intermediate to advanced levels who aim to fine-tune models for clinical diagnosis, disease prediction, and forecasting patient outcomes using structured and unstructured medical data.

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

Generative AI refers to technology capable of producing new content, including text, images, and recommendations, derived from specific prompts and data inputs.

This instructor-led live training, available both online and on-site, is designed for healthcare professionals at beginner to intermediate levels who aim to leverage generative AI and prompt engineering to enhance efficiency, precision, and communication within medical settings.

Upon completing this training, participants will be able to:

• Grasp the core concepts of generative AI and prompt engineering.
• Implement AI tools to streamline clinical, administrative, and research workflows.
• Ensure ethical, secure, and compliant utilization of AI in healthcare environments.
• Refine prompts to achieve consistent and accurate outcomes.

Course Format

• Interactive lectures and discussions.
• Practical exercises and case studies.
• Hands-on experimentation with AI tools.

Course Customization Options

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

This instructor-led, live training in Mexico (online or onsite) is designed for beginner to intermediate-level healthcare professionals, data analysts, and policymakers who wish to understand and apply generative AI within the healthcare sector.

Upon completing this training, participants will be able to:

• Articulate the principles and applications of generative AI in healthcare.
• Identify opportunities where generative AI can enhance drug discovery and personalized medicine.
• Apply generative AI techniques to medical imaging and diagnostics.
• Evaluate 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 integrate seamlessly with clinical processes.

This instructor-led, live training (available online or onsite) targets 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 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 Mexico (online or onsite) is designed for intermediate to advanced healthcare professionals, medical researchers, and AI developers who want 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 streamlined platform designed for executing large language models directly on local hardware.

This guided, live training session (available online or in-person) targets intermediate-level healthcare professionals and IT teams looking to deploy, tailor, and operationalize Ollama-driven AI solutions within clinical and administrative workflows.

After finishing this course, participants will be equipped to:

• Install and configure Ollama to ensure secure usage in healthcare environments.
• Embed local LLMs into clinical processes and administrative operations.
• Adapt models for healthcare-specific terminology and specialized tasks.
• Implement best practices for privacy, security, and regulatory compliance.

Course Format

• Interactive lectures and discussions.
• Live demonstrations and guided hands-on exercises.
• Practical application within a sandboxed healthcare simulation environment.

Customization Options

• For a tailored training version of this course, please contact us to make arrangements.

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

This instructor-led live training (available online or on-site) is designed for intermediate-level professionals aiming 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.
• Assess the clinical relevance, reliability, and safety of outputs generated by TinyML.

Course Format

• Lectures complemented by live demonstrations and interactive discussions.
• Practical exercises using wearable device data and TinyML frameworks.
• Implementation exercises conducted in a guided lab environment.

Course Customization Options

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