Openlayer is an advanced platform designed to build high-quality and trustworthy machine learning models from the ground up. It provides a comprehensive workspace for machine learning testing, observability, goal-setting, and collaboration throughout every stage of development.
Features of Openlayer
- Goal Setting: Openlayer allows users to onboard their data and models and collaborate with their team to set clear expectations regarding quality and performance. Users can define specific goals, such as expecting high performance for a particular subpopulation, and set thresholds for accuracy, precision, and other metrics.
- Validation: Users can quickly understand where there’s room for improvement and prioritize essential goal failures. Openlayer provides a goal timeline, showing the progress over time, and aggregates metrics like AUC ROC, Accuracy, Precision, and Recall.
- Efficient Problem Solving: Openlayer ensures users never get stuck on why goals are failing. It provides all the necessary information to diagnose the root cause of issues.
- Shipping with Confidence: Users can test new commits against their goals to ensure systematic progress without regressions. They can compare different model versions side-by-side to make informed decisions.
- Collaboration: Openlayer promotes team collaboration at every step, from defining project goals to solving individual issues.
- Proactive Bug Detection: The platform encourages users to validate their data and models early to catch mistakes proactively before deployment.
- Data Quality: Openlayer emphasizes using good data over just big data. It helps users understand the exact data needed to enhance model performance and encourages focusing on high-quality and representative datasets.
- Easy Setup: Openlayer offers a 60-second onboarding process where users can upload their models and datasets directly from their training notebooks or pipelines. With a simple API call, models are automatically loaded and deployed.
- Security: Openlayer is SOC2 Type 2 compliant and offers on-premise hosting in a virtual private cloud (VPC), ensuring that models and datasets never leave the user’s infrastructure.
- Product Offerings: Openlayer caters to various machine learning needs, including tabular data and natural language processing (NLP).
- Resources: Openlayer provides a range of resources for its users, including documentation, API references, changelogs, and a dedicated blog.
- Use Cases: The platform is designed for a wide range of professionals, including data scientists, ML engineers, product managers, CTOs, and domain experts.