Deepchecks
ML Model Monitoring

ML model performance is a critical component of a healthy application. To maximize your business
performance, ML and IT teams need to continuously know the status of their model.

Monitor Your Models to Catch
Failures Before Your Users

Keep Your Applications Running

Keep Your Applications Running

When your model is not performing, your
application might suffer a significant impact
which affects your company’s business.
Monitoring the performance of your models is
key in keeping your applications performance
intact. Beyond that, having the capability to
monitor your models from the moment they were
launched throughout the application lifecycle,
including model and data updates is key to
keeping your business performance on target.

Observability

Observability

Observability into your model is key! But, unlike
other services, machine learning models require
much more than just input and output format
validation. Data drift, Concept drift, Performance
deterioration or a broken data pipeline are just a
few examples for common problems that may
arise over time.

Real-Time Alerting

Real-Time Alerting

Your models are in the core of your
value to your customers, be it in healthcare, finance, IT, e-
commerce or other industries. In order to
maintain excellent service to your customers, you
will need to be notified about model issues in real
time and respond as fast as possible.

Quickly Get to The Root Cause
Throughout The Model Lifecycle

Where Is the Issue

Where Is the Issue

You were just informed, either by the customer
success team or by Deepcheck monitoring
system, that your mission critical deployed model
is acting weird. You would like to know if it’s a
data pipeline, a new model version or a change of
data that caused the issue.

Understand the Urgency

Understand the Urgency

The issue shows a model’s abnormal behaviour.
You would like to know what’s the impact: is it a
significant impact on all the data, a specific
segment or a minor portion of the data that only
needs to be monitored. Understanding the scope
of impact is crucial when understanding your
issue’s urgency.

test_dataset, _, _ =Model_version.get_production_dataset(start_time, end_time, data_filters)sns.catplot(text_dataset.data,“feul_type” , hue=“gear”)
Understand the Urgency

Get to the Granular Reason

Once you know where’s the issue, you would like
to dive in and figure out what is the source of
problem. A must step for mitigation. In many
cases this would require to switch from the
Monitoring system UI to your code.

Explainer Video

Meet the Team

Open Source & Community

Deepchecks is committed to keeping the ML evaluation package open-source and community-focused.

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