Industry Challenges

The Public sector is under constant pressure to deliver the best possible service to their citizens, while adhering to strict rules and regulations. Authorities face key challenges, including:

• Secure and Sovereign Data: The public sector deals with sensitive and classified data that cannot leave secured environments. Ensuring data sovereignty, strict access control, and robust encryption is critical for compliance.
• Real-Time Decision Making: Public sectors require real-time AI systems for real-time analytics, insights and personalized user experiences, demanding low-latency, high-throughput data processing on-premises.
• Scalability of AI Infrastructure: Scaling AI infrastructure on-premises can be difficult due to limitations in hardware resources, especially for compute-intensive applications like LLMs and advanced analytics.
• Data Integration: Data in public sectors are often siloed across different departments and systems. Integrating diverse data sources for AI and ML requires sophisticated pipelines and centralized management.

How Hopsworks Solves These Challenges

Air-Gapped Environments 

Hopsworks supports air-gapped environments and runs on-prem, ensuring full data sovereignty and compliance with government security requirements. 

Real-Time Feature Engineering

With real-time feature engineering and sub-millisecond latency, Hopsworks can be used to enable AI systems for real-time fraud detection.

High Availability

Hopsworks' modular AI Lakehouse scales easily with Kubernetes, supporting GPU management for compute-intensive models like LLMs while ensuring high availability.

Unified Data Sources

Hopsworks unifies diverse data sources, providing a centralized data management platform for easy integration and governance.

Use Cases

Personalized Citizen Services 

Automation and personalization of processes like application processing, chatbots for inquiries, and case prioritization.

Real-Time Analytics for City Management

Enhance traffic flow, energy consumption, and waste management using real-time data for analytics and insights. Leverage real-time data for predictive maintenance to optimize maintenance schedules for utilities, transport, and public assets.

Real-Time Fraud Detection 

Detect and prevent fraud in real-time using advanced machine learning models. With sub-millisecond latency, Hopsworks ensures that fraud detection models operate on the freshest data from many different data sources, improving accuracy and reducing false positives.

Why Hopsworks for the Public Sector?

• Air-Gapped and Secure Infrastructure: Built on Kubernetes, Hopsworks is designed to operate seamlessly in air-gapped environments, ensuring compliance with strict data security and sovereignty requirements.
  
• High availability: Carefully designed deployment patterns and best practices for keeping your data safe by ensuring individual component failures do not impact the availability of the Hopsworks cluster.
• ‍Advanced Monitoring and Governance: Hopsworks incorporates capabilities for monitoring data usage, model performance, feature performance and auditing, enabling full transparency between teams and for complying with regulatory AI requirements.

Success Story:

Job Recommendations and Natural Language Processing (NLP) on Job Ads for Arbetsförmedlingen

Challenge

‍Arbetsförmedlingen, the Swedish Public Employment Service, lacked a highly available production environment. To manage and orchestrate workflows and processes around AI, they needed a platform that would allow GPU access and scheduling for both training and serving models, as well as serve online data for recommendations.

Solution

‍They identified Hopsworks as a scalable platform that offers online and offline models, and supports data lake integration. Another important platform capability was that Hopsworks offered multi-tenancy and GDPR-compliant data sharing, suitable for Arbetsförmedlingen’s data sensitive operations.

Results

• Successfully implemented a real-time recommendation system that delivers suitable job postings to approximately 700,000 new job seekers annually in Sweden.
• Developed an effective AI-driven process for detecting discriminatory language in job advertisements.
• Deployed a scalable, multi-tenant platform that ensures GDPR-compliant data sharing across different teams and stakeholders.

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