The best MLflow alternative for computer vision teams

While MLflow tracks your model training runs, it doesn’t explain why your visual AI models fail. FiftyOne is the leading MLflow alternative built for computer vision teams who need more that just experiment tracking. With FiftyOne, you can visually analyze data, discover edge cases, debug data quality issues, and perform model analysis with visual intelligence at the sample level.

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Voxel51 FiftyOne and MLflow are better together

Why you need an MLflow alternative

Traditional experiment tracking can't explain model failures

Experiment tracking tools like MLflow provide valuable metrics and hyperparameter tracking, but they stop short of showing why your model underperforms on specific subsets of data. That’s where FiftyOne, the most powerful MLflow alternative, comes in. FiftyOne reveals hidden patterns in your computer vision data and gives you visual intelligence to fix failure points that traditional experiments tracking can’t uncover.

Sample-level debugging

Take your top training runs from MLflow and inspect failures modes at the sample-level in FiftyOne. It’s the MLflow alternative that will help you discover which data subsets cause failures, identify mislabeled samples, and understand model behavior before deployment.

Complete dataset lineage and versioning

FiftyOne integrates directly with MLflow Artifacts for comprehensive dataset versioning. Build a connected MLOps pipeline where every dataset view, model performance and run insight is linked, giving you an MLflow integration with full dataset lineage

Smarter queries for visual data

Unlike MLflow’s static dashboards, FiftyOne allows you to query your data dynamically. Filter by confidence scores, object sizes, prediction errors, or custom metadata to pinpoint model weaknesses. Supporting teams serious about MLOps for computer vision.

Reference Architecture

Why mature teams use MLflow and FiftyOne together

MLflow tracks how your model was trained. FiftyOne reveals why your model fails. Together they form the perfect MLOps stack for computer vision turning experiments into deployable models.

FiftyOne and MLflow integration from data storage to AI model performance to model deployment

Features

MLflow and FiftyOne are better together

Get a CV stack for deploying visual AI models to production.

Compare features

Track metrics and hyperparameters

Log training artifacts

Model registry integration

Dataset lineage tracking

Compare models side-by-side

Model evaluation metrics

Sample-level performance and debugging

Identify failure modes and edge cases

Data quality and annotation issues

Visual data exploration with metadata

Embeddings and similarity search

“FiftyOne is our primary resource for machine learning research. Thanks to FiftyOne's convenient field visualizations and filtering capabilities, we can easily distinguish incorrect labels and predictions, and therefore iterate on models faster than ever. As a result, we've achieved a 77% reduction in images sent for manual verification.”
Ryan Szeto
Senior Computer Vision Engineer at SafelyYou

“As we developed our Florence-2 model, FiftyOne proved invaluable for data management and visualization. Its powerful capabilities helped streamline our workflow, ensuring we built a robust foundation for our models. Now, as we dive into the development of Florence-5B, we're relying on FiftyOne more than ever. The tool's intuitive interface and rich feature set are essential for effectively managing our large datasets and gaining critical insights.”
Bin Xiao
AI Researcher, Meta (formerly Principal Research Manager, Microsoft GenAI)

“FiftyOne has helped us speed up investigations by 3x. For example, if we see a wrong suction cup grasping an item, we can quickly visualize the issue across all data sources and identify what went wrong.”
Dimitry Pechyoni
Senior Principal Machine Learning Engineer at Berkshire Grey

"FiftyOne enables researchers to analyze and improve the quality of their datasets rapidly, replacing the weeks of manual labor that would otherwise be required without this technology. High-quality data is critical to the success of machine learning systems. Without the right tools to analyze and curate datasets, machine learning development can be inefficient and ineffective.”
Jordi Pont-Tuset
Research Scientist at Google

“At Allstate, my team works on auto vehicle damage inspection. Verifying the damage to a vehicle can take an insurance claim agent hours to verify, but using computer vision and FiftyOne, we can segment the parts of vehicles first, then detect the damages, and finally match the damage to repair costs and generate reports for the adjusters.”
Pavan Nanjundappa
Data Science Manager, Allstate India

“We use FiftyOne to organize large research datasets. My favorite feature is the ability to view distributions over image attributes in the dataset, and filter the dataset by those attributes.”
Brett Israelsen
Principal Research Scientist, AI, Raytheon

“What really stands out about FiftyOne is the flexibility. The plugin framework lets us customize our workflows based on our unique needs, and the mature SDK lets us consolidate more of our pipeline into one tool, avoiding the cost of stitching together multiple systems. FiftyOne integrates directly into our production pipeline to drive 80% reductions in workplace incidents .”
Patrick Rowsome
Head of Computer Vision Operations, Protex AI

FiftyOne is not just an alternative to MLflow—it enhances it

Join leading AI teams using FiftyOne alongside MLflow to accelerate model performance analysis and deployment readiness. Combine the power of experiment tracking with visual data intelligence.

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