Privacy-First AI Systems for Healthcare: From Foundations to Agents
Eric Just
25+ Years Data, Analytics & AI
A Two-Day Hands-On Workshop to Level Up Your AI Architecture Skills
Use AI to unlock more value from your data. Prepare your data to power AI.
AI is changing both how we work with data and how we build the systems around it. Modern models can extract, organize, retrieve, evaluate, and act on information in new ways. That same data powers AI systems with valuable context.
AI can make your data better + Your data can make AI better.
In healthcare, that opportunity comes with a critical constraint: sensitive data demands deliberate choices about where information goes, which models can access it, and how much control you retain.
Privacy-first AI architecture.
This hands-on course explores the building blocks of modern AI systems — from small models to document processing, embeddings, retrieval, RAG, evaluation, and agents — and how to choose among them based on privacy, accuracy, speed, cost, and control.
As agents make AI systems easier to build, understanding the systems themselves is what lets you build the right things.
The goal: understand the systems, protect the data, and put AI to work in ways that are practical for healthcare.
What you’ll learn
Move Beyond Prompting and Learn How Modern AI Systems Are Built
Run modern AI models on commodity hardware
Build privacy-first workflows for sensitive data
Reduce dependence on cloud-based AI services
Understand the strengths and tradeoffs of different AI model types
Learn the difference between decoders, encoders, cross-encoders, and extraction models
Evaluate AI approaches based on accuracy, speed, cost, and privacy
Transform unstructured documents into structured data
Extract valuable information from clinical text
Combine text, documents, and structured data into intelligent workflows
Create a Retrieval-Augmented Generation database from real documents
Understand retrieval, reranking, and context engineering
Learn where RAG works—and where it doesn't
Explore real-world challenges working with clinical and genomic data
Extract structured information from complex clinical documents
Build a multimodal pipeline combining clinical and laboratory data
Learn directly from Eric
Eric Just
25+ Data Practitioner: Developer, Architect, Executive, Founder. AI Builder.
Who this course is for
Data Engineers, Analysts, and Architects: You build data pipelines or analyze data and want to understand how AI can enhance data workflows.
Managers, Directors, and Data Leaders: You want to understand how AI is changing the capabilities, tools, and skills of modern data teams.
Product Managers: You manage and make decisions about data-focused products or applications.
What's included
Live sessions
Learn directly from Eric Just in a real-time, interactive format.
Lecture + Lab-based instruction
Slide materials are presented in an approachable way. When we discuss an approach, we present alternative approaches because there is more than one way with this rapidly evolving toolkit. Labs are engaging and help students see how things work. Students can run/manipulate/build on code. Lab work is python-based in Jupyter notebooks.
Hands-on Lab Environment: Yours to Keep After Course
All you need is browser access. You will get an email with a link to your lab environment, containing code you will run. At the conclusion, you will be able to download your containerized lab environment and run it locally... forever.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Maven Guarantee
Your purchase is backed by the Maven Guarantee.
Course syllabus
2 live sessions • 7 lessons
Week 1
Sep
22
Day 1: Foundations — Models, Control, and Documents
Day 1
Sep
23
Day 2: Application — Retrieval, Measurement, Agents, and Safety
Day 2
Schedule
Live sessions
12 hrs
All course content and hands-on work is part of the live sessions. We will work through examples together.
Tue, Sep 22
3:00 PM—9:00 PM (UTC)
Wed, Sep 23
3:00 PM—9:00 PM (UTC)
Frequently asked questions
Feedback from Students
⭐⭐⭐⭐⭐ 4.8/5 (35)
"The sessions gave me a much clearer understanding of key concepts -- local LLM setup, building user interfaces, making API calls, and working with embeddings and vector databases, etc. It opened a new door for me in thinking about how AI can support oncology research."
-- Senior Design Engineer, Huntsman Cancer Institute
"The feedback has been entirely positive. One comment I keep getting is that people didn’t realize that there are useful applications of LLMs that do not require powerful GPUs."
-- Director, Comprehensive Oncology Data & Engineering (CODE), Huntsman Cancer Institute
"It was impressive that you built something that started very accessible and built up into real world examples. I feel more confident in engaging in our AI convos at Manifold."
-- Product Manager, Manifold.ai
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