Our goal is to democratise the use of AI. Hopsworks is ideal for small and medium-sized businesses that lack complex team infrastructures. These teams often operate within budget constraints but may benefit from leveraging the full potential of AI for products and projects. Our MLOps platform allows companies to seamlessly adopt and manage Machine Learning Systems within the shortest possible time frame, minimal resource dedication and lower cost.

1. Automation and Orchestration

Feature engineering, storage, and serving are automated, thereby reducing the need for manual intervention. This streamlines workflows and enables small teams to focus on what matters most which is model development instead of infrastructure.

2. Improved Productivity

With built-in versioning, feature reuse, and automated workflows, Hopsworks for small teams allows data scientists and engineers to work more efficiently, eliminating repetitive tasks.

Productivity is increased through: 

• Reducing Silos: Hopsworks centralizes feature storage, making data and machine learning (ML) assets accessible across teams. This ensures collaboration between data engineers, scientists, and analysts, preventing knowledge hoarding.
• Decreasing Time-to-Market: Pre-built feature pipelines and automation accelerate the deployment of ML models, helping small teams bring AI-driven solutions to production faster.
• Increasing Data Reuse Across Models: Teams can recycle and share pre-engineered features across different ML models, eliminating the need to create features from scratch for each project.

6. Reduces Feature Engineering Workload

By providing precomputed, reusable, and real-time features, our platform reduces the time spent on data preparation, a major bottleneck in ML projects.

7. Reduces Tools Complexity

This is an end-to-end solution which consolidates feature stores, metadata management, ML pipelines, and monitoring into a single platform, reducing the need for multiple disconnected tools.

8. Increases Efficiency

With automated pipelines, optimized storage, and real-time feature serving, teams can deliver models faster with fewer errors and manual interventions.

9. Monitoring of the Data and Automated Rollbacks

Our platform tracks feature drift and data quality issues, automatically rolling back to previous feature versions when anomalies are detected.

10. Decreases Technical Debt, Promoting Best Practices

Enforces feature versioning, lineage tracking, and governance, reducing the accumulation of unmanageable code and poor practices.

11. Easier Maintenance

A centralized feature store with automated monitoring simplifies maintenance, reducing the need for constant manual updates and debugging.

12. Easier Governance

Comes with built-in access control, auditing, regulatory and security compliance tracking.

13. Cost Effectiveness with Higher Quality

Reduces duplicated work through process automation and error prevention.

‍