Welcome to the mlops-workflow! This tool provides a structured approach to managing machine learning projects. It covers everything from training models to deploying them. Whether you're tracking experiments or registering models, this workflow simplifies the process.
Follow these simple steps to get started with mlops-workflow on your computer.
To download the software, you will need to visit the releases page.
Click here to visit the downloads page.
On the releases page, look for the latest version. You will find the installation files there.
- Visit the releases page: Download here
- Select the latest version: Look for the newest release at the top of the page.
- Download the appropriate file based on your operating system (Windows, macOS, or Linux).
- Run the downloaded file: Double-click the file to start the installation process. Follow the on-screen instructions to complete installation.
Before you install, please check the following system requirements:
- Operating System: Windows 10 or later, macOS 10.15 or later, or any recent version of Linux.
- RAM: Minimum 8GB recommended for optimal performance.
- Disk Space: At least 1GB of free space to install the software and store your projects.
- Python: Ensure that Python 3.7 or newer is installed, as this is essential for model training and inference.
mlops-workflow offers a variety of features to enhance your machine learning projects:
- Model Training: Easily train machine learning models using popular libraries like PyTorch.
- Inference: Quickly deploy models for real-time predictions.
- Experiment Tracking: Log and track your experiment results efficiently.
- Model Registry: Organize and manage different model versions with ease.
- Integration: Compatible with PostgreSQL for data storage and MinIO for object storage.
After installation, you will see a user-friendly interface. Here’s a brief overview of the main components:
- Dashboard: This is your command center. View all your projects and their statuses here.
- Experiments Tab: Track past experiments easily. Compare results and analyze performance metrics.
- Models Tab: Access your registered models. You can deploy or update them from this section.
- Settings: Adjust configurations such as database connections and logging preferences.
Here are some common issues you may face and how to resolve them:
- Installation Fails: Ensure you have enough disk space and that your operating system is compatible. Check for any missing dependencies and install them as needed.
- Python Not Found: If you receive a Python error during startup, verify that Python 3.7 or newer is installed and added to your system's PATH.
- Connection Errors: If the application can’t connect to the database, double-check your settings under the Settings tab.
We welcome contributions to improve mlops-workflow. If you wish to contribute, please follow the guidelines below:
- Fork the repository: Create a personal copy of the project by clicking the fork button.
- Make your changes: Develop your features or fix bugs on your forked repository.
- Submit a pull request: Once you are ready, submit a pull request to the main repository.
If you need help, please don’t hesitate to reach out. You can contact us via GitHub issues or email us at support@mlops-workflow.com. We are here to assist you.
Join our growing community of users and developers. Engage with fellow users, share your projects, and seek advice. Follow our discussions on our GitHub page and in associated forums.
Now that you've installed the mlops-workflow, it's time to explore its capabilities. Start by importing your data and running your first model.
For more detailed instructions on specific tasks, please refer to the documentation within the application.
Enjoy your experience with mlops-workflow!