Provides a cloud-agnostic MLOps platform that automates machine learning workflows through CI/CD practices, enabling version-controlled experimentation, hybrid/multi-cloud orchestration, and seamless integration with existing systems. This platform reduces infrastructure management overhead, ensuring reproducibility and scalability while allowing data science teams to focus on model development and optimization.
Funding
$2.5M 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
Data science teams face challenges in managing complex machine learning workflows, including version control, reproducibility, and scalability across diverse infrastructure environments. The lack of automation in these processes leads to increased overhead, hindering model development and deployment efficiency.
Solution
Valohai provides a cloud-agnostic MLOps platform that streamlines machine learning workflows through CI/CD practices. The platform automates experimentation, orchestration, and deployment, ensuring reproducibility and scalability across hybrid and multi-cloud environments. By automating the ML lifecycle, Valohai reduces infrastructure management overhead and enables data science teams to focus on model development and optimization. The platform supports any language or framework and integrates with existing CI/CD systems via API and webhooks.
Target Audience
The primary customers are data scientists, machine learning engineers, and MLOps teams seeking to automate and scale their machine learning workflows across diverse infrastructure environments.
Features
- Framework-agnostic ML execution across on-premises, single-cloud, multi-cloud, and hybrid-cloud environments
- Automatic versioning of every run to preserve a complete lineage of work, including models, datasets, and metrics
- Orchestration of ML workloads on any infrastructure with a single click, command, or API call
- TypeScript SDK and React chatbox plugin for rapid, low-code integration
- Integration with various data and model sources, including Snowflake, Redshift, BigQuery, V7 Labs, and Labelbox
- REST API and webhooks for integration with existing CI/CD pipelines and other systems
- Audit Log for comprehensive event tracking related to all AI/ML development, ensuring traceability and accountability