Industry Challenges

To conduct detailed research analysis and support critical decision-making, the research and healthcare industry requires access to and the ability to process vast amounts of sovereign, real-time data. Some key challenges the industry is facing include:

• Data Silos, Fragmentation and Collaboration: Data is often distributed across multiple systems, making it difficult to integrate for AI and ML workflows. The industry is also faced with limitations for interdisciplinary collaboration between research teams contributing to slow innovation.
• Data Quality and Standardization: Inconsistent formats, missing values, and varying standards obstruct model training and insights.
• Data Privacy and Compliance: Strict personal data regulations create challenges in sharing and processing sensitive patient data securely.
• Scalability and Performance: Handling large datasets, such as medical imaging or genomic data, requires robust infrastructure.

How Hopsworks Solves These Challenges

Breaking Down Data Silos and Fostering Collaboration

Hopsworks enables healthcare AI by centralizing data, providing shared workspaces, and version-controlled feature stores, which allows interdisciplinary teams to collaborate efficiently and securely.

Improving Data Quality

Built-in tools for data validation, lineage, and governance ensure high-quality, standardized datasets for accurate model training.

Ensuring Privacy and Compliance

‍Hopsworks enforces robust data governance, encryption, and compliance measures, making it easy to adhere to GDPR, HIPAA, and other data privacy regulations.

Scalable AI Lakehouse Infrastructure

Designed to handle large-scale data workloads, Hopsworks supports high-performance GPU clusters for tasks like medical imaging and genomic analysis.

Use Cases

Medical Co-Pilot

Utilize real-time data, based on individual patients observations and medical history, to automate, analyze and develop personalized treatment plans.

Predictive Analytics

Forecast hospital admissions or disease outbreaks using historical data to manage workforce and resource logistics.

Accurate Disease Detection

Use AI models for detecting diseases like cancer in X-rays, MRIs, and CT scans with high accuracy by analysing and comparing large volumes of patient data. 

Why Hopsworks for Research and Healthcare?

• Process Unstructured Data: Automate tedious reporting tasks and avoid overlooking critical information by processing large volumes of unstructured data .
• Sovereign Data Control: Deploy on any infrastructure—cloud, hybrid, or on-premises—ensuring data sovereignty and flexibility.
• Sub-Millisecond Latency: Ensure real-time decision-making with ultra-low latency for disease detection, research analysis, and more.
• Feature Freshness: Keep machine learning models updated with the latest data for accurate predictions.

Success Story:

Data Preparation, Cataloging, and Feature Management for a Massive Genomic Dataset at Karolinska Institute

Challenge

‍The Karolinska Institute’s Center for Cervical Cancer Prevention analyzes massive volumes of omics data to uncover insights into the biology of viruses. For individual research groups or universities, processing such vast datasets is logistically challenging and often unfeasible. Addressing these challenges requires standardized solutions and international collaboration at scale.

Solution

‍The Karolinska Institute adopted Hopsworks to manage genomic data and conduct secure, GDPR-compliant research studies. By organizing research into projects, Hopsworks provides a secure, collaborative environment for medical studies on shared clusters. Its design optimizes for commodity hardware, enabling cost-effective scalability to petabyte-scale datasets. Furthermore, Hopsworks supports both commodity and enterprise GPUs for deep learning, making it adaptable to the computational demands of modern genomic research.

‍Results

• 90% cost reduction was achieved through cost savings associated with storing large volumes of data and CPUs and GPUs for data processing.
• Easy collaboration between researchers when managing, sharing, and processing genomic data.
• Massively parallel data processing pipeline for massive genomic datasets.

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