AI Data Management & Evaluation Platform
Zeno is an interactive platform for exploring and managing your data, debugging your models, and tracking and comparing model performance.
Sponsors and Organizations
Explore
Explore data and model outputs with customizable views for any data type
Evaluate
Interactively discover, test and save model behavior for analysis and updates
Report
Create exportable visualizations and charts comparing models and slices
Explore your data
Zeno's modular instance view can be extended to render any data type and model output
Create interactive reports
Track and compare performance across slices and models
Slices created in the Exploration page can be used to build interactive visualizations for deeper analyses of model behavior. Visualizations include bar charts for comparing slice performance across models and trend tables for detecting regressions in slice performance.
Zeno charts can be exported as PDFs or PNGs for sharing with other stakeholders, or shared as links for live views of model performance.
Extend Zeno with the Python API
Add new models, metrics, and metadata columns with the Python API
The Python API is used to add models, metrics, and new metadata columns to Zeno.
@model functions wraps Python libraries such as PyTorch, Tensorflow, Keras, HuggingFace, etc. to get model predictions
@metric functions are used to calculate different metrics on slices of data
@distill functions derive new metadata columns from your data instances.
- Model
- Metric
- Distill
@model
def load_model(model_path):
model = whisper.load_model("tiny")
def pred(df, ops: ZenoOptions):
# Get a list of paths for each audio file
files = [os.path.join(ops.data_path, f) for f in df[ops.data_column]]
return [model.transcribe(f)["text"] for f in files]
return pred
@metric
def accuracy(df, ops: ZenoOptions):
if len(df) == 0:
return 0
# Calculate the accuracy of the model if not empty
return 100 * (df[ops.label_column] == df[ops.output_column]).sum() / len(df)
@distill
def amplitude(df, ops: ZenoOptions):
# Get a list of paths for each audio file
files = [os.path.join(ops.data_path, f) for f in df[ops.data_column]]
amps = []
for audio in files:
# Load each audio file and calculate the amplitude
y, _ = librosa.load(audio)
amps.append(float(np.abs(y).mean()))
return amps