Curating high-quality datasets is the best way to develop robust ML models. Renumics Spotlight helps your team to do this for your engineering and manufacturing data.
import pandas as pd
from renumics.backstage.dataset
import Dataset
df = pd.read_csv("some.csv")
with Dataset("myDataset.h5", 'w')
as dataset:
dataset.
from_dataframe(df, index=False)
Import you data and enrich it with embeddings from pre-trained models with our easy-to-use Python API. Many engineering data types are supported out of the box.
Interactively explore your data and adapt the visualizations on the fly. Identify and annotate segments, outliers and correlations in your data.
Identify relevant populations to plan data acquisition and labeling campaigns. Assess the possibility for data augmentation and synthetic data.
import pandas as pd
from renumics.backstage.dataset
import Dataset
df = pd.read_csv("some.csv")
with Dataset("myDataset.h5", 'w')
as dataset:
dataset.
from_dataframe(df, index=False)
Build your dataset with our easy-to use Python API. Use the embedding of your model as a similarity measure and optionally add more information and metadata.
Use your team's domain knowledge to understand segments, edge cases and outliers. Choose which data should be annotated or acquired next.
Iterate the data boosting process until performance goals are met. Build user trust by clearly understanding the limits of the model.
How do I know that Renumics Spotlight is right for me?
Daniel Klitzke
Jun 23 • 5 min
Daniel Klitzke
May 24 • 6 min
Daniel Klitzke
Mar 24 • 4 min