Product Tour
Video Demo
Watch the video to learn how Fiddler AI Observability provides data science and MLOps teams with a unified platform to monitor, analyze, explain, and improve machine learning models at scale, and build trust in AI.
Documented UI Tour
When you log in to Fiddler, you are on the Home page and you can visualize monitoring information for your models across all your projects.
At the top of the page, you will see donut charts for the number of triggered alerts for Performance, Data Drift, and Data Integrity.
To the right of the donut charts, you will find the Bookmarks as well as a Recent Job Status card that lets you keep track of long-running async jobs and whether they have failed, are in progress, or successfully completed.
The Monitoring summary table displays your models across different projects along with information on their traffic, drift, and the number of triggered alerts.
View all of your bookmarked, Projects, Models, Charts, and Dashboards by clicking "View All" on the Bookmarks card on the homepage or navigating directly to Bookmarks via the navigation bar.
Track all of your ongoing and completed model, dataset, and event publish jobs by clicking "View All" on the Jobs card on the homepage or navigating directly to the Jobs via the navigation bar.
On the side navigation bar, below charts, is the Projects Tab. You can click on the Projects tab and it lands on a page that lists all your projects contained within Fiddler. See the Fiddler Samples section below for more information on these projects. You can create new projects within the UI (by clicking the “Add Project” button) or via the Fiddler Client.
Projects represent your organization's distinct AI applications or use cases. Within Fiddler, Projects house all the Models specific to a given application, and thus serve as a jumping-off point for the majority of Fiddler’s model monitoring and explainability features.
Go ahead and click on the bank_churn to navigate to the Project Overview page.
Here you can see a list of the models contained within the fraud detection project, as well as a project dashboard to which analyze charts can be pinned. Go ahead and click the “churn_classifier” model.
From the Model Overview page, you can view details about the model: its metadata (schema), the files in its model directory, and its features, which are sorted by impact (the degree to which each feature influences the model’s prediction score).
You can then navigate to the platform's core monitoring and explainability capabilities. These include:
Monitor — Track and configure alerts on your model’s performance, data drift, data integrity, and overall service metrics. Read the Monitoring documentation for more details.
Analyze — Analyze the behavior of your model in aggregate or with respect to specific segments of your population. Read the Analytics documentation for more details.
Explain — Generate “point” or prediction-level explanations on your training or production data for insight into how each model decision was made. Read the Explainability documentation for more details.
Fiddler Samples
Fiddler Samples is a set of datasets and models that are preloaded into Fiddler. They represent different data types, model frameworks, and machine learning techniques. See the table below for more details.
Project
Model
Dataset
Model Framework
Algorithm
Model Task
Explanation Algos
Bank Churn
Bank Churn
Tabular
scikit-learn
Random Forest
Binary Classification
Fiddler Shapley
Heart Disease
Heart Disease
Tabular
Tensorflow
Binary Classification
Fiddler Shapley, IG
IMDB
Imdb Rnn
Text
Tensorflow
BiLSTM
Binary Classfication
Fiddler Shapley, IG
Iris
Iris
Tabular
scikit-learn
Logistic Regression
Multi-class Classification
Fiddler Shapley
Lending
Logreg-all
Tabular
scikit-learn
Logistic Regression
Binary Classification
Fiddler Shapley
Logreg-simple
Tabular
scikit-learn
Logistic Regression
Binary Classification
Fiddler Shapley
Xgboost-simple-sagemaker
Tabular
scikit-learn
XGboost
Binary Classification
Fiddler Shapley
Newsgroup
Christianity Atheism Classifier
Text
scikit-learn
Random Forest
Binary Classification
Fiddler Shapley
Wine Quality
Linear Model Wine Regressor
Tabular
scikit-learn
Elastic Net
Regression
Fiddler Shapley
DNN Wine Regressor
Tabular
Tensorflow
Regression
Fiddler Shapley
See the README on GitHub for more information.
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