Valohai · 0 reviews

Valohai

Unified MLOps platform for scalable machine learning development and deployment

MLOps Platforms

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About Valohai

Valohai is a comprehensive MLOps platform designed to streamline the entire machine learning lifecycle from experimentation to production deployment. The platform consolidates essential ML tools into a single, intuitive environment, enabling teams to manage experiments, track model performance, automate workflows, and scale infrastructure efficiently. Valohai provides centralized experiment tracking, versioning control for datasets and models, distributed training capabilities, and seamless deployment pipelines. By integrating with AiDOOS, organizations gain enhanced governance through unified access management, optimized cost allocation across ML projects, streamlined integration with enterprise systems, and improved visibility into ML operations at scale. The platform accelerates development cycles, reduces infrastructure complexity, and enables data-driven decision-making through comprehensive logging and analytics.

Challenges It Solves

ML teams struggle with fragmented tools and inconsistent experiment tracking across projects
Scaling machine learning infrastructure requires complex configuration and manual management
Lack of visibility into model performance, data lineage, and resource utilization across teams
Difficulty reproducing experiments and managing model versions in production environments
High operational costs from inefficient resource allocation and unoptimized training workflows

Faster experiment iteration and deployment cycles

Reduced infrastructure management overhead

Improved model reproducibility and governance

Use Cases

Computer Vision Model Development

Accelerate image classification and object detection projects with distributed training and experiment comparison across multiple architectures and hyperparameters.

72% Reduced model training time from weeks to days

NLP & Language Model Fine-tuning

Manage large-scale language model training and fine-tuning with centralized experiment tracking and version control for datasets and model checkpoints.

58% Improved model quality through systematic experiment management

Production Model Monitoring

Monitor deployed models for performance degradation and data drift with automated alerting and rollback capabilities.

81% Detected and resolved model issues 40% faster

Collaborative ML Research

Enable data science teams to collaborate on experiments with shared environments, version control, and centralized artifact management.

65% Enhanced team productivity through transparent experiment sharing

Pricing

Pricing available on request

Valohai pricing is customized based on your team size, integrations, and requirements. AiDOOS will get you a scoped proposal — for free.

Schedule a Meeting

Key Features

Experiment Tracking & Versioning

Comprehensive tracking of all ML experiments with automatic versioning

100% reproducibility of experiments and model configurations

Distributed Training

Scale training across multiple GPUs and nodes seamlessly

Up to 10x faster training times with automatic resource optimization

Model Registry & Versioning

Centralized repository for model artifacts and metadata

Simplified model promotion from dev to production stages

Workflow Automation

Define and automate complex ML pipelines with YAML configuration

Eliminate manual intervention and reduce deployment errors by 90%

Performance Monitoring

Real-time dashboards for tracking model metrics and data drift

Proactive issue detection and rapid response to performance degradation

Scalable Infrastructure

On-demand compute resources with automatic scaling capabilities

Pay only for resources used with intelligent load balancing

Reviews

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Enterprise Readiness

Role-Based Access Control

Encryption in Transit & at Rest

Audit Logging

Secure API Authentication

Data Isolation

Integrations

7 total apps

Native integration for container orchestration and distributed training across Kubernetes clusters

Seamless integration with TensorFlow training scripts and automatic logging of metrics

Native support for PyTorch training with automatic artifact and checkpoint management

Integration for large-scale data processing and distributed training workflows

Data storage and artifact management with S3 bucket integration

Version control integration for code and configuration tracking

Notifications and alerts sent to Slack channels for experiment completion and alerts

AiDOOS Managed Deployment

Deploy Valohai in

AiDOOS handles setup, CRM integration, SSO config, and user provisioning. Your team goes live — not your IT department.

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Deployments

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Adoption rate

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Post-deploy sat.

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Time to value

Prerequisites

Configuration Options

Virtual Delivery Center · A new delivery category

A Virtual Delivery Center for Valohai

Pre-vetted experts and AI agents in the loop, assembled as a delivery pod. Pay in Delivery Units — universal pricing across roles, seniority, and tech stacks. No hiring, no contracting, no procurement cycle.

Plans from $2,000 — Starter Pack, 10 Delivery Units, 90 days
Refundable on unused Delivery Units, anytime — no questions asked
Re-delivery guarantee on acceptance miss
Pre-flight delivery sizing — you see the plan before you commit

Get a delivery plan for Valohai What’s a Virtual Delivery Center?

How a Virtual Delivery Center delivers Valohai

Outcome-based delivery via AiDOOS’s VDC model. Why VDC vs traditional consulting? →

Outcome-Based

Pay for results, not hours

Milestone-Driven

Clear deliverables at each phase

Expert Network

Access to certified specialists

Implementation Timeline

Discover

Requirements & assessment

Integrate

Setup & data migration

Validate

Testing & security audit

Rollout

Deployment & training

Optimize

Performance tuning

Schedule a Meeting

Frequently Asked Questions

How does Valohai integrate with existing ML infrastructure?

Valohai connects to Kubernetes clusters, cloud platforms, and on-premise infrastructure via agents. Through AiDOOS integration, you gain unified governance across all ML resources and simplified access management across your organization.

What machine learning frameworks does Valohai support?

Valohai supports TensorFlow, PyTorch, scikit-learn, XGBoost, and any custom Python-based training code. The platform is framework-agnostic and automatically captures metrics and artifacts.

Can Valohai handle large-scale distributed training?

Yes. Valohai supports distributed training across multiple GPUs and nodes with automatic resource allocation. You can scale from single-machine experiments to multi-node clusters seamlessly.

How does Valohai ensure model reproducibility?

Valohai automatically versions code, data, environment, hyperparameters, and outputs for every experiment. This ensures complete reproducibility and audit trails for regulatory compliance.

What happens after a model is deployed?

Valohai provides real-time performance monitoring, data drift detection, and automated alerting. You can track metrics, compare against baseline models, and trigger retraining workflows automatically.

How does AiDOOS enhance Valohai deployment?

AiDOOS provides centralized governance, unified billing across ML projects, streamlined integration with enterprise systems, and improved visibility into resource utilization and costs.