Open source tooling for data-centric AI on unstructured data
What is Data-Centric AI?
Data-centric AI is a paradigm for developing machine learning (ML) solutions focused on the engineering of the data used to build AI systems. The term, coined by Andrew Ng, emphasizes the importance of systematically enhancing training datasets by leveraging insights from trained ML models. At Renumics, we believe that DCAI is vital for creating real-world AI systems that produce tangible value.
Why Use Open Source Tools?
The key to successful DCAI is finding tools that are both efficient and user-friendly for daily applications. This curated collection is designed to help you discover open-source tools instrumental for building your data-centric AI workflows on unstructured data (like images, audio, video, and text).
Scope of This Collection
- Includes tools with an open-source license that are actively maintained.
- Covers tools useful for building DCAI workflows on various types of unstructured data.
- Offers a collection of workflow snippets aimed at illustrating typical tasks solved using these tools.
- Excludes specific topics, such as tooling for tabular data, dedicated labeling tools, and MLOps tooling.
Contributing
If you notice something that could enhance this list, we’re eager to hear from you. Please contribute by contacting us or submitting a pull request.
Tooling Categories
- Data Versioning
- Embeddings and Pre-Trained Models
- Visualization and Interaction
- Outlier and Noise Detection
- Explainability
- Active Learning
- Uncertainty Quantification
- Bias and Fairness
- Observability and Monitoring
- Augmentation and Synthetic Data
- Security and Robustness
Example Tools
Data Versioning
Data Version Control (DVC)DVC is a command-line tool and VS Code extension to develop reproducible machine learning projects.
DeepLakeA data lake for deep learning that helps in building, managing, querying, versioning, and visualizing datasets.
Embeddings and Pre-Trained Models
- Towhee – Makes neural data processing pipelines simple and fast.
- TensorFlow Hub – A repository of reusable assets for ML.
- Hugging Face Transformers – State-of-the-art ML for Pytorch, TensorFlow, and JAX.
Troubleshooting Guide
While embarking on your DCAI journey, you may encounter challenges. Here are some troubleshooting tips:
- Ensure that your tools are up to date and actively maintained.
- If you experience missing features, check the tool’s documentation or consult user forums.
- For installation issues, verify that your system meets the required specifications for the tools in use.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Conclusion
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
