ClearML Hyper-Datasets
Build Data-Centric AI workflows
Make the most out of your unstructured-data using queryable datasets
Leverage unstructured data
Data-Centric model development
- Upload or register existing files to any supported storage solution including object-storage / http / shared-folder
- Add ground-truth annotations and structured meta-data (json)
- Create snapshots (a.k.a versions)
- Query and explore your data through the web UI
- Slice, split, and sort based on meta-data as part of your training code
- Refine using different slicing and dataset mixtures
- Introduce automation on any dataset version changes
Learn how Neural Guard developed a methodology to build datasets for DL models that optimize ROI and TTM to increase competitiveness in their market.
Review Neural Guard’s process and how ClearML Hyper-Datasets granted all needed functionality in a single, highly integrated MLOps toolchain to build specialized pipelines and connect to both research and production environments.
Case study
Hyper-Datasets and beyond
ClearML Hyper-Datasets integrates seamlessly with ClearML Experiment and ClearML Orchestrate, leveraging end-to-end cross-department visibility in your research, development, and production.
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