ML Monitoring for your Models in Production

Deploying ML Models
Made Easy

Deploying machine learning models can be complex, but Aporia's observability platform simplifies the process with real-time model monitoring, data drift detection, and model versioning and experiment tracking. With Aporia, you can optimize your models for maximum performance and make data-driven decisions about how to improve them over time. Whether you're an experienced data scientist or new to ML deployment, Aporia's intuitive interface makes it easy to get started.

Deploy your machine learning models with confidence and ensure they are performing at their best, always.

Thousands of data science & ML teams use us to achieve their AI goals

See Your Models Shine in Production

More than just a Monitoring platform

Monitor, Visualize, & Improve Your Models in Production


Centralized, real-time view of model health & performance

Live Alerts

Detect drifts, bias and data integrity issues


Get to the root cause and improve models with production data


Know the why behind your models predictions

Start Monitoring Your Models in Minutes

Simplify ML Model Deployment,
Trust Your AI

Build Production-Grade Machine Learning Models

Machine learning models undergo performance degradation right after deployment. Therefore, it is important to identify and examine the issues earlier to manage them effectively. Get a single pane of glass with everything you need to know about your models in production with Aporia. Instantly build custom dashboards for your recommender systems, combining both business and data science metrics.

Detect Drift, Bias & Data Integrity Issues

Track the changes in the deployed model predictions to prevent and resolve deterioration. Aporia's platform helps you detect and address data drift by providing tools for monitoring and visualizing changes in data distribution. You can then retrain your models to adapt to new data and maintain their accuracy and performance.

Understand Which Features Have the Greatest Impact

Aporia provides customizable monitoring and observability for your machine learning models, enabling ML teams to fully trust their models and ensure that they are working as intended. With dynamic widgets and custom metrics, you can monitor prediction drifts, data drifts, missing values at input, freshness, F1 Score, etc.

Support Any Use Case

You know your models and use case best. Customize monitoring based on your specific requirements to ensure your models stay on track and perform at their peak.

Customer LTV

Monitor behavior patterns, segment customers, and take action accordingly to ensure that the customers you invest in generate revenue and profits.

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Dynamic Pricing

Boost sales and respond quickly to changes in market demand, inventory levels, and competitor pricing to ensure you’re not overpricing potential customers.

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Recommender Systems

Scale recommender systems in production. Drive more revenue, increase conversions and engagement, and enhance trust in your models’ recommendations.

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Lead Scoring

Ensure your Lead Scoring Model is providing its intended value so that you can nurture the leads that will actually lead your business to success.

Churn Prediction

Win back customers by increasing retention rate and accurately predicting customer churn rate with

Credit Risk

Ensure that your Credit Risk Model performs as intended so that you can rest easy knowing that your loans are compliant, fair, and repaid (with interest, of course).

Fraud Detection

Ensure your Fraud Detection Model works as intended so that you keep the customers that generate your revenue and profit -- and ditch the bad eggs.

Demand Forecasting

Improve your business's ability to capitalize on ever-changing demand and ensure that you aren't missing out on sales nor losing money on wasted inventory


Ensure your Language Models are performing as intended with real-time model monitoring, live drift alerts, and explainability.


Aporia naturally fits into your workflow

Amazon S3 Amazon S3
Glue Glue
Snowflake Snowflake
BigQuery BigQuery
Databricks Databricks
Azure Blob Storage Azure Blob Storage
Redshift Redshift
PostgreSQL PostgreSQL
Athena Athena
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
Feast Feast
Weights & Biases Weights & Biases
ClearML ClearML

Data Sources

Amazon S3





Azure Blob Storage




Delta Lake



DevOps Infrastructure Monitoring

New Relic




Cloud Providers

Azure ML

Vertex AI

AWS Sagemaker

ML Frameworks



Hugging Face

Scikit Learn


dmlc XGBoost


Spark MLib



Microsoft Teams






MLOps - Complementary MLOps Tools






Weights & Biases


Loved By

See why data scientists, ML engineers, and R&D love using Aporia.

Aporia is a leader in AI & Machine Learning Operationalization on G2
Orr Shilon

ML Engineering Team Lead

“As a company with AI at its core, we take our models in production seriously. Aporia allows us to gain full visibility into our models’ performance and take full control of it.”

Orr Shilon

ML Engineering Team Lead

Aviram Cohen


“ML models are sensitive when it comes to application production data. This unique quality of AI necessitates a dedicated monitoring system to ensure their reliability. I anticipate that similar to application production workloads, monitoring ML models will – and should – become an industry standard.”

Aviram Cohen


Guy Fighel

General Manager AIOps

“With Aporia’s customizable ML monitoring, data science teams can easily build ML monitoring that fits their unique models and use cases. This is key to ensuring models are benefiting their organizations as intended. This truly is the next generation of MLOps observability.”

Guy Fighel

General Manager AIOps

Daniel Sirota

Co-Founder | VP R&D

“ML predictions are becoming more and more critical in the business flow. While training and benchmarking are fairly standardized, real-time production monitoring is still a visibility black hole. Monitoring ML models is as essential as monitoring your server’s response time. Aporia tackles this challenge head on.”

Daniel Sirota

Co-Founder | VP R&D

Lukas Olson

Data Scientist

“We develop and deploy models that impact students’ lives across the country, so it’s crucial that we have good insight into model quality while ensuring data privacy. Aporia made it easy for us to monitor our models in production and conduct root cause analysis when we detect anomalous data.”

Lukas Olson

Data Scientist

Carlos Leyson

Data Scientist

“As an early stage startup, starting to launch ML models in the fintech sector, monitoring the predictions and changes in our data is critical, and Aporia has made it easy by providing the right integrations and is easy to use.”

Carlos Leyson

Data Scientist

Start using Aporia now