This startup offers MLOps solutions that streamline the deployment and management of machine learning models in production environments. By optimizing the workflow from model development to deployment, it minimizes operational bottlenecks and improves the reliability of AI applications.
Funding
$33M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Organizations struggle to effectively deploy and manage machine learning models in production, leading to operational bottlenecks and unreliable AI applications. Existing MLOps solutions often lack the flexibility to adapt to dynamic environments and fail to provide adequate governance and auditability. This results in delayed deployments, increased costs, and difficulty in ensuring model accuracy and compliance.
Solution
Striveworks offers Chariot, a low-code MLOps platform that streamlines the entire AI lifecycle, from model development to deployment, monitoring, and governance. Chariot enables organizations to embed structured, scalable model development and management into existing workflows, accelerating the deployment of models from months to hours. The platform's 'Process as Code' and Data Lineage system ensures governance and auditability across the entire analytics lifecycle, tracking the provenance of data and models, as well as the usage of model inferences. By democratizing data-driven decisions, Chariot brings multidisciplinary teams together to ensure models are effective and aligned with organizational goals.
Target Audience
Striveworks targets Fortune 500 firms and public sector leaders seeking a comprehensive MLOps solution to manage model development, monitoring, and governance, and to ensure models solve real-world challenges.
Features
- Low-code interface for rapid model building and deployment
- Data and model lineage tracking for governance and auditability
- Flexible deployment options to accommodate various environments
- Automated retraining workflows to maintain model accuracy with fresh data
- Real-time monitoring of model performance to detect and address issues proactively
- Integration with existing workflows to embed MLOps into organizational processes
- Support for computer vision, natural language processing, and generative AI solutions
- Role-based access control to ensure data security and compliance