Prompt Engineering for 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 processes, and ensure rigorous evaluation through its integrated libraries and services.

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

By the conclusion of this training, participants will be capable of:

• Applying supervised fine-tuning techniques to Gemini models in Vertex AI.
• Implementing prompt management workflows that include versioning and testing.
• Utilizing evaluation libraries to benchmark and optimize AI performance.
• Deploying and monitoring enhanced models in production environments.

Course Format

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

Customization Options

• For customized training on this topic, please contact us to arrange.

Agentic AI represents a methodology where artificial intelligence systems autonomously plan, reason, and execute actions using various tools to achieve specific objectives within established boundaries.

This instructor-led training session, available both online and on-site, is designed for healthcare and data teams at an intermediate level. It focuses on helping participants design, assess, and govern agentic AI solutions tailored for clinical and operational scenarios.

Upon completing this training, participants will be equipped to:

• Articulate the core concepts and limitations of agentic AI within the healthcare sector.
• Create secure agent workflows incorporating planning, memory management, and tool integration.
• Develop retrieval-augmented agents capable of processing clinical documents and knowledge bases.
• Assess, monitor, and govern agent behavior through the implementation of guardrails and human-in-the-loop oversight.

Course Format

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

Course Customization Options

• For organizations seeking a customized version of this training, please reach out to us to arrange a tailored experience.

This instructor-led live training in Argentina, available online or on-site, is designed for intermediate to advanced healthcare professionals and AI developers who wish to implement AI-driven healthcare solutions.

By the end 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 live, instructor-led training in Argentina (online or in-person) is designed for intermediate healthcare professionals who aim to apply AI and AR/VR solutions for medical education, surgical simulations, and rehabilitation.

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

• Comprehending how AI improves AR/VR applications in healthcare.
• Leveraging AR/VR for surgical simulations and medical education.
• Utilizing AR/VR tools in patient rehabilitation and therapeutic contexts.
• Examining the ethical and privacy issues associated with AI-enhanced medical technologies.

This instructor-led, live training in Argentina (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 Argentina (online or on-site) is designed for intermediate-level healthcare professionals and data scientists who wish to comprehend and implement AI technologies in healthcare environments.

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

• Identify critical 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 Argentina (online or onsite) is designed for healthcare professionals and researchers who aim 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 Argentina (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 Argentina (online or onsite) is designed for intermediate to advanced medical AI developers and data scientists who aim 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 capable of:

• Fine-tuning AI models on healthcare datasets, including EMRs, imaging, and time-series data.
• Implementing transfer learning, domain adaptation, and model compression techniques within medical contexts.
• Addressing privacy concerns, bias, and regulatory compliance during model development.
• Deploying and monitoring 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 Argentina (online or onsite) is designed for beginner to intermediate-level healthcare professionals, data analysts, and policymakers 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, enabling interoperability, and developing decision-support systems that align with medical processes.

This instructor-led training, available online or onsite, targets intermediate to advanced professionals aiming to design, implement, and manage LangGraph-based healthcare solutions while navigating 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 (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.

Course Format

• Interactive lectures and discussions.
• Hands-on exercises featuring real-world case studies.
• Implementation practice within a live-lab environment.

Customization Options

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

This instructor-led, live training in Argentina (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:

• Grasp the role of multimodal AI in contemporary healthcare.
• Combine structured and unstructured medical data for AI-driven diagnostics.
• Use AI techniques to analyze medical images and electronic health records.
• Create predictive models for diagnosing diseases and recommending treatments.
• Implement speech recognition and natural language processing (NLP) for medical transcription and patient communication.

Ollama is a lightweight platform designed for running large language models locally.

This instructor-led live training, available both online and onsite, targets intermediate-level healthcare practitioners and IT teams looking to deploy, customize, and operationalize Ollama-based AI solutions within clinical and administrative settings.

Upon completing this training, participants will be able to:

• Install and configure Ollama for secure use in healthcare environments.
• Integrate local LLMs into clinical workflows and administrative processes.
• Customize models for healthcare-specific terminology 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 within a sandboxed healthcare simulation environment.

Customization Options

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

TinyML refers to the integration of machine learning algorithms into low-power, resource-constrained wearable and medical devices.

This instructor-led live training, available either online or on-site, targets intermediate-level professionals seeking to implement TinyML solutions for healthcare monitoring and diagnostic applications.

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

• Designing and deploying TinyML models for real-time health data processing.
• Collecting, preprocessing, and interpreting biosensor data to generate AI-driven insights.
• Optimizing models specifically for low-power and memory-limited wearable devices.
• Assessing 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 implementation exercises within a lab environment.

Customization Options

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