How Omada Well being scaled affected person care by fine-tuning Llama fashions on Amazon SageMaker AI
This submit is co-written with Sunaina Kavi, AI/ML Product Supervisor at Omada Well being.
Omada Health, a longtime innovator in digital healthcare supply, launched a brand new vitamin expertise in 2025, that includes OmadaSpark, an AI agent skilled with sturdy scientific enter that delivers real-time motivational interviewing and vitamin schooling. It was constructed on AWS. OmadaSpark was designed to assist members establish their very own motivational challenges like emotional consuming, enhance meals choices, set objectives, and maintain lasting conduct change. The next screenshot exhibits an instance of OmadaSpark’s Dietary Training characteristic, demonstrating how members obtain customized vitamin schooling in actual time.
On this submit, we study how Omada partnered with AWS and Meta to develop this healthcare-aligned AI answer utilizing Llama fashions on Amazon SageMaker AI. We discover the technical implementation, structure, and analysis course of that helped Omada scale customized vitamin steering whereas sustaining their dedication to evidence-based care.
The chance for AI-powered vitamin steering
Diet schooling serves as a cornerstone of Omada’s persistent situation administration applications. Though well being coaches excel at offering customized care, the rising demand for fast, handy dietary info offered a chance to reinforce our coaches’ affect via know-how. Omada sought an modern answer that might complement their coaches’ experience by dealing with routine analytical duties, so they might focus extra deeply on significant member interactions. The purpose was to offer instant, high-quality vitamin schooling whereas sustaining strict healthcare compliance with Omada’s care protocols and the private touches that makes their program efficient.
Omada Well being’s OmadaSpark goals to assist members establish real-world emotional and sensible limitations to wholesome consuming in at present’s atmosphere, the place ultra-processed meals are prevalent and diets can fail to ship long-term outcomes. OmadaSpark options motivational interviewing,utilizing questions to assist members establish their very own objectives, reinforce autonomy, and discover motivation to vary habits. OmadaSpark’s Dietary Training characteristic can scale back the psychological load of real-time meals choices and encourage members to steadily incorporate more healthy meals options. Omada’s vitamin expertise provides up to date monitoring capabilities, like water monitoring, barcode scanning, and photo-recognition know-how that supply versatile and non-restrictive help designed to advertise a wholesome relationship to meals.
“We see AI as a power multiplier for our well being coaches, not a substitute,” explains Terry Miller, Omada’s Vice President, Machine Studying, AI and Information Technique. “Our collaboration with AWS and Meta allowed us to implement an AI answer that aligns with our values of evidence-based, customized care.”
Resolution overview
Omada Well being developed the Dietary Training characteristic utilizing a fine-tuned Llama 3.1 mannequin on SageMaker AI. The implementation included the Llama 3.1 8B mannequin fine-tuned utilizing Quantized Low Rank Adaptation (QLoRA) strategies, a fine-tuning methodology that enables language fashions to effectively be taught on smaller datasets. Preliminary coaching used 1,000 question-answer pairs created from Omada’s inner care protocols and peer reviewed literature and specialty society pointers to offer evidence-based dietary schooling.
The next diagram illustrates the high-level structure of Omada Well being’s Llama implementation on AWS.
The answer workflow consists of the next high-level steps:
- The Q&A pairs for dietary schooling datasets are uploaded to Amazon Simple Storage Service (Amazon S3) for mannequin coaching.
- Amazon SageMaker Studio is used to launch a coaching job utilizing Hugging Face estimators for fine-tuning Llama 3.1 8B mannequin. QLoRA strategies are used to coach the mannequin and mannequin artifacts saved to Amazon S3.
- The inference workflow is invoked via a consumer query via a cellular shopper for OmadaSpark’s dietary schooling characteristic. A request is invoked to fetch member private knowledge primarily based on the consumer profile in addition to dialog historical past, in order that responsive info is customized. For instance, a roast beef recipe gained’t be delivered to a vegetarian. On the similar time, this characteristic doesn’t present medical info that’s associated to a selected particular person’s medical state of affairs, comparable to their newest blood glucose take a look at. The SageMaker AI endpoint is invoked for vitamin era primarily based on the member’s question and historic conversations as context.
- The mannequin generates customized vitamin schooling, that are fed again to the cellular shopper, offering evidence-based schooling for folks in Omada’s cardiometabolic applications..
- For analysis of the mannequin efficiency, LangSmith, an observability and analysis service the place groups can monitor AI utility efficiency, is used to seize inference high quality and dialog analytics for steady mannequin enchancment.
- Registered Dietitians conduct human assessment processes, verifying scientific accuracy and security of the vitamin schooling offered to customers. Upvoted and downvoted responses are considered in LangSmith annotation queues to find out future fine-tuning and system immediate updates.
The next diagram illustrates the workflow sequence in additional element.
Collaboration and knowledge fine-tuning
A essential facet of Omada Well being’s success with AI implementation was the shut collaboration between their scientific group and the AI improvement group. Omada AI/ML Product Supervisor Sunaina Kavi, a key determine on this collaboration, highlights the significance of this synergy:
“Our work with the scientific group was pivotal in constructing belief and ensuring the mannequin was optimized to fulfill real-world healthcare wants,” says Kavi. “By carefully engaged on knowledge choice and analysis, we made positive that OmadaSpark Dietary Training not solely delivered correct and customized vitamin e but additionally upheld excessive requirements of affected person care.
“The AWS and Meta partnership gave us entry to state-of-the-art basis fashions whereas sustaining the self-hosted management we want in healthcare, for privateness, safety, and high quality functions. The fine-tuning capabilities of SageMaker AI allowed us to adapt Llama to our particular vitamin use case whereas preserving our knowledge sovereignty.”
Affected person knowledge safety remained paramount all through improvement. Mannequin coaching and inference occurred inside HIPAA-compliant AWS environments (AWS is Omada’s HIPAA Enterprise Affiliate), with fine-tuned mannequin weights remaining underneath Omada’s management via mannequin sovereignty capabilities in SageMaker AI. The AWS safety infrastructure offered the inspiration for implementation, serving to keep affected person knowledge safety all through the AI improvement lifecycle. Llama fashions provided the pliability wanted for healthcare-specific customization with out compromising efficiency. Omada centered their technical implementation round SageMaker AI for mannequin coaching, fine-tuning, and deployment.
Lastly, Omada carried out rigorous testing protocols, together with common human assessment of mannequin outputs by certified. Omada launched your complete workflow with the mannequin in 4.5 months. All through this course of, they constantly monitored response accuracy and member satisfaction, with iterative fine-tuning primarily based on real-world suggestions.
Enterprise affect
The introduction of OmadaSpark considerably boosted member engagement of those who used the instrument. Members who interacted with the vitamin assistant had been 3 times extra prone to return to the Omada app on the whole in comparison with those that didn’t work together with the instrument. By offering round the clock entry to customized dietary schooling, Omada dramatically lowered the time it took to deal with member vitamin questions from days to seconds.
Following their profitable launch, Omada is deepening their partnership with AWS and Meta to broaden AI capabilities together with fine-tuning fashions, context window optimization, and including reminiscence. They’re creating a steady coaching pipeline incorporating actual member questions and enhancing AI options with extra well being domains past vitamin.
“Our collaboration with AWS and Meta has proven the worth of strategic partnerships in healthcare innovation,” shares Miller. “As we glance to the longer term, we’re excited to construct on this basis to develop much more modern methods to help our members.”
Conclusion
Omada Well being’s implementation demonstrates how healthcare organizations can successfully undertake AI whereas addressing industry-specific necessities and member wants. Through the use of Llama fashions on SageMaker AI, Omada amplifies the humanity of well being coaches and additional enriches the member expertise. The Omada, AWS, and Meta collaboration showcases how organizations in extremely regulated industries can quickly construct AI purposes through the use of modern basis fashions on AWS, the trusted healthcare cloud supplier. By combining scientific experience with superior AI fashions and safe infrastructure, they’ve created an answer that may rework care supply at scale whereas sustaining the customized, human-led strategy that makes Omada efficient.
“This mission proves that accountable AI adoption in healthcare is not only attainable—it’s important for reaching extra sufferers with high-quality care,” concludes Miller.
Omada stays dedicated to rising its human care groups with the effectivity of AI-enabled know-how. Wanting forward, the group is devoted to creating new improvements that foster a way of real-time help, confidence, and autonomy amongst members.
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Concerning the authors
Sunaina Kavi is an AI/ML product supervisor at Omada, devoted to leveraging synthetic intelligence for conduct change to enhance outcomes in diabetes, hypertension, and weight administration. She earned a Bachelor of Science in Biomedical Engineering and an MBA from the College of Michigan’s Ross College of Enterprise, specializing in Entrepreneurship and Finance. Previous to transitioning to Omada, she gained expertise as an funding banker in Know-how, Media, and Telecom in San Francisco. She later joined Rivian, specializing in charging options inside their infotainment group, and based her personal startup aimed toward utilizing AI to handle autoimmune flares. Sunaina can also be actively concerned within the Generative AI group in San Francisco, working to reinforce security, safety, and systematic evaluations throughout the healthcare neighborhood.
Breanne Warner is an Enterprise Options Architect at Amazon Net Providers supporting healthcare and life science (HCLS) clients. She is captivated with supporting clients to make use of generative AI on AWS and evangelizing mannequin adoption for first-party and third-party fashions. Breanne can also be Vice President of the Ladies at Amazon with the purpose of fostering inclusive and various tradition at Amazon. Breanne holds a Bachelor of Science in Laptop Engineering from the College of Illinois Urbana-Champaign.
Baladithya Balamurugan is a Options Architect at AWS targeted on ML deployments for inference and utilizing AWS Neuron to speed up coaching and inference. He works with clients to allow and speed up their ML deployments on providers comparable to Amazon SageMaker and Amazon EC2. Primarily based out of San Francisco, Baladithya enjoys tinkering, creating purposes and his homelab in his free time.
Amin Dashti, PhD, is a Senior Information Scientist at AWS, specializing in mannequin customization and coaching utilizing Amazon SageMaker. With a PhD in Physics, he brings a deep scientific rigor to his work in machine studying and utilized AI. His multidisciplinary background—spanning academia, finance, and tech—allows him to sort out complicated challenges from each theoretical and sensible views. Primarily based within the San Francisco Bay Space, Amin enjoys spending his free time along with his household exploring parks, seashores, and native trails.
Marco Punio is a Sr. Specialist Options Architect targeted on GPU-accelerated AI workloads, large-scale mannequin coaching, and utilized AI options on AWS. As a member of the Gen AI Utilized Sciences SA group at AWS, he makes a speciality of high-performance computing for AI, optimizing GPU clusters for basis mannequin coaching and inference, and serves as a worldwide lead for the Meta–AWS Partnership and technical technique. Primarily based in Seattle, Washington, Marco enjoys writing, studying, exercising, and constructing GPU-optimized AI purposes in his free time.
Evan Grenda Sr. GenAI Specialist at AWS, the place he works with top-tier third-party basis mannequin and agentic frameworks suppliers to develop and execute joint go-to-market methods, enabling clients to successfully deploy and scale options to resolve enterprise agentic AI challenges. Evan holds a BA in Enterprise Administration from the College of South Carolina, a MBA from Auburn College, and an MS in Information Science from St. Joseph’s College.
