MLOps Workflow
The main features, capabilities and usage recommendations of MLOps Workflow are described in this documentation.
MLOps Workflow streamlines the machine learning lifecycle, focusing on data version control, reproducibility, and collaboration. It provides an easy-to-use visual interface that integrates data management, experiment tracking, and model evaluation.
Why do you need trial tracking and version control?
Data evolution: Datasets change over time, and tracking these changes is critical to maintaining model accuracy.
Avoid confusion: Repeated iterations can lead to errors if teams lose track of which data and model versions were used.
Reproducibility: Proper tracking ensures that results can be reproduced and shared with team.
1. Data Version Control
Data version control simplifies managing changes in datasets throughout the project lifecycle.
Capabilities:
Track changes and create backups.
Restore previous states with a single click.
Centralized tracking of data evolution.
How It Works:
Create versions at any stage—data upload, annotation, or transformation.
Each version is stored in a secure binary format, avoiding file duplication.
Restore data by creating a new project version from a previous state.
Note: Available only on Pro and Enterprise plans.
2. MLOps Workflow Visualization
The visual map shows the entire data lifecycle, including the apps and operations that modify it.
Key Features:
Data Operations:
Augmentation (cropping, rotation, adding noise, etc.).
Annotation transformation.
Training data generation.
Dataset splitting, merging, and filtering.
Navigation: Easily access data versions, projects, and application sessions.
Example:
Data Import: Upload and annotate images using tools like Smart Tool.
Version Creation: Capture the project's state after annotation.
Model Training: Train YOLOv10 while automatically generating checkpoints and reports.
Deployment: Apply the model to new datasets or export results.
3. Model Benchmarking
The platform generates automatic reports to evaluate model performance using metrics such as:
mAP, Precision, Recall.
Inference speed.
Classification accuracy and IoU.
Benefits:
Compare model versions to identify improvements.
Understand how architecture or hyperparameter changes affect results.
Building a Workflow
Best Practices:
Use Projects Instead of Individual Datasets: This improves Workflow readability.
Optimize Nodes: Combine file cards into folders to reduce redundancy.
Add Descriptions: Provide context for each Workflow element.
Avoid Overwriting Node States: Maintain a clear history of changes.
Organize Session-Based Applications: Prevent clutter and simplify Workflow structure.
Practical Usage
Workflow Entry Points:
Project context menu.
Task context menu.
Workspace options.
Integrating Workflows into Applications:
Detailed instructions for integrating Workflows into your custom applications are available on the Supervisely Developer Portal.
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