Valohai
UnclaimedThe scalable MLOps platform enabling CI/CD for ML and pipeline automation on-prem and any-cloud.
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TL;DR - Valohai
- Automates ML workflows with CI/CD principles for reproducibility and scalability.
- Supports hybrid and multi-cloud deployments, including on-premises infrastructure.
- Provides automatic versioning for code, data, experiments, and models with full lineage.
Pricing: Paid only
Best for: Enterprises & pros
Pros & Cons
Pros
- Ensures full reproducibility of ML experiments and models
- Offers flexibility to run ML workloads on any cloud or on-premises infrastructure
- Simplifies MLOps by abstracting infrastructure management
- Supports any ML framework, language, or library via Docker containers
- Provides unlimited projects, experiments, pipelines, and deployments with per-user pricing
Cons
- Pricing details are not transparently listed and require a custom quote
- Requires integration with existing systems, which might involve initial setup efforts
Key Features
Automatic versioning with complete lineage of ML experiments, datasets, and modelsHybrid and multi-cloud support for AI workload managementSmart orchestration of ML workloads on any infrastructure (cloud or on-premise)Framework and language agnostic development environmentCI/CD pipelines for ML automationModel deployment for batch and real-time inferenceHyperparameter optimizationHuman-in-the-loop workflows
Pricing Plans
Free TrialPer-User License
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- Unlimited Projects
- Unlimited Experiments
- Unlimited Pipelines
- Unlimited Deployments
- Auto-scaling Compute
- On-premises or Managed Cloud deployment options
- GPU Optimization
- Advanced Security and Access Control
- Technical Support
- Training and Onboarding
About Valohai
By Toolradar Team·
Valohai is a comprehensive MLOps platform designed to streamline complex machine learning workflows from experimentation to deployment. It provides end-to-end automation and reproducibility for ML projects, functioning as a CI/CD system specifically tailored for machine learning. The platform supports hybrid and multi-cloud environments, allowing users to manage AI workloads across various cloud providers and on-premises data centers with ease.
Valohai is built for ML pioneers, data scientists, and MLOps teams seeking to accelerate ML development and deployment. It abstracts away the complexities of managing ML infrastructure, enabling users to focus on building and deploying models. Key benefits include automatic versioning of experiments, datasets, and models, smart orchestration of ML workloads on diverse infrastructures, and a developer-centric core that supports any language or framework.
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Valohai FAQ
How does Valohai ensure the reproducibility of ML experiments across different environments?
Valohai automatically versions every aspect of an ML run, including code, data, logs, hyperparameters, and the environment. This complete lineage tracking ensures that any experiment can be systematically reviewed and reproduced, even months later, regardless of the underlying infrastructure.
Can Valohai manage ML workloads on a combination of private cloud and on-premises GPU clusters?
Yes, Valohai is designed for hybrid and multi-cloud environments. It can orchestrate ML workloads seamlessly across various cloud providers, private clouds, and on-premises data centers, including optimizing GPU allocation on your existing hardware.
What mechanisms does Valohai provide for integrating with existing CI/CD systems or custom tools?
Valohai offers robust APIs and webhooks that allow for deep integration with existing CI/CD pipelines and any other internal systems. This enables triggering pipelines, managing resources, and automating workflows programmatically, ensuring flexibility in your development ecosystem.
How does Valohai handle data versioning and curation without duplicating large datasets?
Valohai allows users to curate and version datasets efficiently. While it tracks changes and lineage for datasets, it focuses on smart management to avoid unnecessary duplication, ensuring that data scientists can collaborate on and compare different versions of data without excessive storage overhead.
Does Valohai support specific machine learning frameworks or is it truly agnostic?
Valohai is framework and language agnostic. It can run anything you can put into a Docker container, meaning you can use any ML framework (TensorFlow, PyTorch, scikit-learn, etc.), any programming language (Python, R), and any external libraries without restriction.
What kind of cost optimization features are included for managing compute resources?
Valohai includes auto-scaling compute resources that dynamically adjust based on workload needs to optimize costs. It also provides tools to track costs and usage in real-time, including underutilization alerts, to help manage spending effectively across different infrastructures.
Source: valohai.com