What Is Data-Centric Development?
Data-centric development is a methodology that focuses on defining which projects or systems should be produced using available data. It differs from the more typically used model-centric approach in the following ways:
- The primary objective is working on data
- Data consistency is essential
- Reduces noisy data instead of collecting more data
- Data quality is iterated
- Code or algorithms are fixed
With a data-centric development strategy, using high-quality data—by identifying inconsistencies, labeling data correctly, and removing redundancies—significantly improves a model’s accuracy. Often, it results in better predictions or outcomes than repeatedly adjusting the model trained on a faulty dataset.
Use Cases for Data-Centric Development
Adopting a data-centric approach for the machine learning lifecycle is challenging, which is why many enterprises shy away from such a strategy. However, there are two instances where it works best:
- High-level Customization: Industries like manufacturing and healthcare require models with a high degree of customization to produce accurate results. Moreover, each model has to be trained on specific datasets instead of a general one.
- Large Datasets: Today’s enterprises deal with big volumes of data in their operations. However, there is no assurance that data quality is high, and noisy datasets will probably skew results. Shifting towards a data-centric paradigm instead of a model-centric one ensures the model is trained only on relevant, reliable data.
How to Implement a Data-Centric Approach
Below are best practices when shifting towards data-centric development:
- Label Data Properly: Labels are related to specific values applied to data, and they provide important context to the datasets used by your model. Assigning the wrong labels will affect how your model is trained, affecting predictions.
- Augment Data: Machine learning models need to work with large datasets to produce favorable outcomes. Adding more data to train your model is crucial, but you have to eliminate noise that causes high variance and inaccuracy.
- Use Version Control Tools: Improving data quality involves making changes that create newer versions of your dataset. Keep track of the revisions done with a version control tool so you won’t have to use an error-filled, noisy dataset.
Data-Centric Development with Pachyderm
Achieve a data-centric development approach for your machine-based learning models, starting with better data management. Pachyderm offers top-notch version control and data lineage to ensure your team can track changes more efficiently.Book a demo today to learn how your team can scale your machine learning life cycle with a data-centric paradigm.« Back to Glossary Index