Get the model and training platform registered, for calculating production drift due to data, to route the rectified version back into the training process , improving model accuracy, creating a continuous feedback loop.
Datalier formats your framework to get Model and training platform registered to monitor model inaccuracy, by getting the prediction logs back into the observability layer to identify any drift. Root cause for the drift gets traced to the data, which is rectified, and re-versioned closing the loop without a manual handoff between teams.
Datalier exports training-ready data straight into your platform, in the format your framework expects. Once training runs are completed, predictions logs are flown back into the platform for identifying drifts impacting the model accuracy. Furthermore, the root cause data for the drifts get traced, which are then rectified using the relabelling feedback loop, and are re-versioned to be pushed back into the training platform creating a model monitoring and performance loop without a manual handoff.
Amazon SageMaker, Google Vertex AI, Azure ML, HuggingFace, LangSmith, and any custom platform via webhook or REST API.
Once your model has finished a training run, its predictions are logged back into our platform and compared against expected ground outcomes. When accuracy drifts, the underlying data is traced, flagged, and routed back into Datalier for re-labeling, automatically, not as a separate manual process.
A measurable drop in prediction confidence or accuracy against ground truth, tracked continuously by the Observe layer. When it crosses a configured threshold, the specific underlying records are flagged for correction not the whole dataset.
COCO, YOLO, JSONL, image-folder layouts, and Parquet, whatever your training framework already expects, with no custom export scripts to maintain.
Ongoing. The connection stays live for the life of the project, every retraining cycle pulls the latest corrected version, and every production signal routes back the same way.