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Monitoring your Databricks Models

Aporia’s ML Observability platform integrates natively with your Databricks for quick and easy model monitoring

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7 minutes and you’re up!

Data Science and ML teams from emerging enterprises to Fortune-500 organizations rely on Aporia to monitor their Machine Learning models in production. Aporia offers quick and simple deployment and monitors billions of predictions with low cloud costs.

01

Deploy Aporia with a
Databricks API key

02

Integrate your models
with Databricks SQL

03

Start using
Aporia!

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End-to-End ML

By deploying Aporia on Databricks, ML teams gain full visibility into their models in production out of the box. They can detect, troubleshoot, and eliminate production issues faster, as well as proactively improve their models based on feedback and insights from production.

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Start Monitoring Your Models in Minutes

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More than just a Monitoring platform

Monitor, Visualize, & Improve Your Models in Production

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Visibility

Centralized, real-time view of model health & performance

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Live Alerts

Detect drifts, bias and data integrity issues

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Investigation

Get to the root cause and improve models with production data

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Explainability

Know the why behind your models predictions

Why Aporia and Databricks?

ML Dashboards

Visualize & Share models performance

Get a unified view of all your models under a single hub. Keep an eye on model activity, inference trends, data behavior, model performance (f1 score, Precision, RMSE, etc).

Learn more

Live Alerts

Detect drift, bias & data integrity issues

Get live alerts to Slack / MS Teams on any drift, bias, performance, or data integrity issues.

Learn more

Explainability

Explain model predictions

Discover which features impact your predictions the most, and easily communicate model results to key stakeholders

Learn more
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Integrations

Aporia naturally fits into your workflow

DevOps
Amazon S3 Amazon S3
Glue Glue
Snowflake Snowflake
BigQuery BigQuery
Databricks Databricks
Azure Blob Storage Azure Blob Storage
Redshift Redshift
PostgreSQL PostgreSQL
PostgreSQL PostgreSQL
Delta Lake Delta Lake
Spark Spark
Ray Ray
New Relic New Relic
Grafana Grafana
Prometheus Prometheus
DataDog DataDog
Azure ML Azure ML
Vertex AI Vertex AI
AWS Sagemaker AWS Sagemaker
TensorFlow TensorFlow
LightGBM LightGBM
Hugging Face Hugging Face
Scikit Learn Scikit Learn
PyTorch PyTorch
dmlc XGBoost dmlc XGBoost
CatBoost CatBoost
Spark MLib Spark MLib
Slack Slack
Microsoft Teams Microsoft Teams
Jira Jira
PagerDuty PagerDuty
Opsgenie Opsgenie
Email Email
Webhook Webhook
MLFlow MLFlow
Kubeflow Kubeflow
KServe KServe
DVC DVC
Feast Feast
Weights & Biases Weights & Biases
ClearML ClearML

Data Sources

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Amazon S3

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Glue

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Snowflake

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BigQuery

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Databricks

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Azure Blob Storage

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Redshift

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PostgreSQL

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PostgreSQL

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Delta Lake

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Spark

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Ray

DevOps Infrastructure Monitoring

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New Relic

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Grafana

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Prometheus

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DataDog

Cloud Providers

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Azure ML

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Vertex AI

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AWS Sagemaker

ML Frameworks

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TensorFlow

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LightGBM

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Hugging Face

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Scikit Learn

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PyTorch

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dmlc XGBoost

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CatBoost

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Spark MLib

Alerting

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Slack

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Microsoft Teams

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Jira

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PagerDuty

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Opsgenie

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Email

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Webhook

MLOps - Complementary MLOps Tools

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MLFlow

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Kubeflow

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KServe

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DVC

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Feast

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Weights & Biases

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ClearML

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Start using Aporia now

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