The Rise of Machine Learning (ML) Model Monitoring Platforms

Several companies struggle while implementing, managing, and deploying learning models to an extent. The complexity aggravates when various stages in the process, like the IT operators, data scientistsML monitoring engineering teams, as well business groups work in silos. 

All such challenges have led companies to shift their attention from all the business models to taking care of ML model-specific management requirements. Due to all these, the MLOPs have emerged. 

The MLOPs are repose at the intersection of data engineering, machine learning, and DevOps. It mainly focuses on the entire lifecycle of the model development and its usage. 

Machine Learning (ML) Model Monitoring Platforms
Machine Learning (ML) Model Monitoring Platforms

This also includes the various aspects of machine learning model deployment and operations. The important components of MLOps include model versioning, model lifecycle management, model monitoring, model discovery, model security, and governance.

Model Monitoring- An Overview

Model monitoring is considered to be the operational stage in the life cycle of machine learning. This comes precisely after model deployment.

It requires monitoring the ML models for changes like data drift, concept drift, and data drift. It also ensures the model is keeping an allowable level of performance.

Machine learning models are usually deployed to automate critical business processes and automate decisions. This includes loan approval, claims to process, and fraud detection.

However, these theoretical ML models can change if the production data is split from the data used to train the model.

Model monitoring also closely keeps track of the performances of all the ML models during production. This process of tracking and monitoring helps AI teams to identify all the potential issues beforehand and thus mitigate the downtime.

This is why ML model monitoring platforms are gaining popularity rapidly.

The framework is model monitoring creates an all-important feedback loop. ML monitoring in machine learning models helps in deciding to make the decision whether an update is required or can be continued with the existing models.

Machine Learning model monitoring platforms

Some of the popular ML monitoring platforms are :


 Qualdo is a machine-learning management tool, also known as a performance ML monitoring tool used in Google, AWS, and Azure. It helps in extracting insights. 

This extraction happens from the Machine Learning input or prediction data for improving model performance. Qualdo integrates with many Artificial Intelligence, communication tools, and machine learning to make collaboration easier.

ML Works: 

This Machine Learning model management tool from Artificial Intelligence firm Tredence qualifies MLOps at scale. 

ML Works also offers other features for orchestration, model generation, monitoring, and deployment. It also allows white-box model monitoring and deployment in order to ensure total provenance review, transparency, and explainability.

Amazon SageMaker Model Monitor: 

Amazon SageMaker Model Monitor tool has the capability to detect and report all the inaccuracies automatically in the deployed models which are deployed in production. 

The Amazon Sagemaker tools have other features like customizable data monitoring and customizable data collection, metrics visualization, built-in analysis in order to detect drift, scheduling monitoring jobs, and model prediction.


This is a lightweight ML monitoring tool used for tracking and managing ML model metadata. Neptune also offers a store, version, model development metadata, and query model.

This can also compare with parameters and metrics for producing anomalies.

What is ML model monitoring?
What is ML model monitoring?

Frequently Asked Questions

Here are some frequently asked questions related to ML monitoring platforms

What is ML model monitoring?

Model Monitoring is a stage in the machine learning lifecycle that occurs after model deployment. It entails continuously monitoring your ML models for adjustments such as model degradation, information drift, and concept drift, and making sure that your model is performing at an acceptable level.

What is AI model monitoring?

How do ML models improve performance?

7 Methods to Boost the Accuracy of a Model
  1. Add more data. Having more data is always a good idea
  2. Treat missing and Outlier values
  3. Feature Engineering
  4. Feature Selection
  5. Multiple algorithms
  6. Algorithm Tuning
  7. Ensemble methods
Some of the methods that can be applied on the data side are as follows:
  1.  Acquire more data
  2.  Missing value treatment
  3. Outlier treatment
  4. Feature engineering
  5. Hyperparameter tuning
  6. Applying different models
  7. Ensembling methods
  8.  Cross-validation

Final Thoughts

At the end of this article, you have got an idea of why ML model monitoring platforms are becoming popular. Also, as you know how to evaluate all the tools for ML monitoring, you can choose the right one for your organization by making a proper choice.