The tool also promises to reduce project costs through features like project-level cost budgeting, hybrid-cloud workflow distribution, and underutilized resource management. It offers actionable insights through intelligent alerting based on model metrics and real-time metrics from experiments to deployments. The tool also aims to speed up the market launch with white-glove support over Slack, unique datasets for active learning, and independent deployment to production. It can be set up with existing workflows and tools in less than 60 minutes and offers additional features like multi-cloud support, Kubernetes support, and Docker support. The tool also emphasizes its security with regular vulnerability assessments and penetration testing.
Key takeaways:
- The company offers a solution to the challenges of putting ML models into production, including time loss, high costs, and lack of support.
- Their tool provides a command center for ML, enabling faster experimentation, seamless data connection, and powerful infrastructure at scale.
- They manage the entire ML lifecycle, helping to reduce project costs, provide actionable insights, and speed up market entry.
- The company ensures security with regular vulnerability assessments and penetration testing, and fits into existing workflows in less than 60 minutes.