Machine learning model monitoring is the operational phase following model deployment in the machine learning lifecycle. This includes changes to ML models, such as model deterioration, data drift, and idea drift, and ensuring that the model is still performing well. Several model monitoring software tools are available to monitor these model changes. Let’s take a look at some of the most useful ML model monitoring tools.
Neptune AI is an MLOps company designed for research and product teams that conduct large numbers of experiments. It can organize training and production metadata according to a given priority using its versatile metadata structure. It can also create dashboards that provide hardware and performance metrics and allow model comparisons. Almost any ML metadata, including metrics and losses, prediction images, hardware measurements, and interactive visualizations, can be logged and displayed using Neptune.
Arise AI is a tool for monitoring ML models that can improve project observability and assist users in product AI troubleshooting. This enables ML engineers to robustly upgrade current models. Additionally, it provides a pre-launch validation toolbox that can run pre- and post-launch validation checks and gain confidence in the model’s performance. In addition, it provides automatic model monitoring and simple integration.
WhyLabs is a model visualization and monitoring tool that helps ML teams keep track of data pipelines and ML applications. It helps detect data bias, data drift, and data quality degradation. This eliminates the need for manual problem-solving, saving time and money in the process. Regardless of scale, this tool can be used to work with both structured and unstructured data.
Qualdo is a tool for tracking the performance of machine learning models on Google, AWS, and Azure. Users can track the progress of their models throughout their lifecycle using Qualdo. Qualdo allows users to derive insights from product ML input/prediction data, logs, and application data to monitor and enhance your model’s performance. It also uses Tensorflow’s data validation and model evaluation capabilities and provides tools to track the performance of the ML pipeline in Tensorflow.
Fiddler is a model monitoring tool with an intuitive, complex UI. It enables users to manage complex machine learning models and datasets, deploy machine learning models at scale, interpret and debug model predictions, examine model behavior for full data and slices, and monitor model performance. It provides users with basic information about how well their ML service product performs. Fiddler users can set up alerts for a model or collection of models in a project to inform them of production issues.
Seldon Core is an open source platform for implementing machine learning models on Kubernetes. This framework is independent, works in any cloud or on-premises, and supports the best machine-learning toolkits, libraries, and languages. Additionally, it converts your machine learning models (ML models) or language wrappers (Java, Python) into production REST/GRPC microservices. Thousands of production machine learning models can be packaged, deployed, tracked and managed using this MLOps platform.
Anodot is an AI monitoring tool that automatically understands data. This program is designed from the ground up to ensure data interpretation, analysis, and correlation to improve the operations of any business. It monitors many things simultaneously, including revenue, partners, and telco networking.
Clearly an open source ML model monitoring system. It helps to analyze machine learning models during their design, validation, or production monitoring. A pandas DataFrame is used by the tool to produce interactive reports. It helps evaluate, test and track the effectiveness of ML models from validation to production. Explicitly includes monitors that collect information from the deployed ML service, including model metrics. It can be used to create dashboards for real-time monitoring.
With Sensius, an AI model observability platform, users can track the entire ML pipeline, decode predictions, and proactively address issues for better business outcomes. Using Sensible Monitors, it automates continuous model monitoring for performance, drift, outliers, and data quality concerns. Additionally, customers can receive real-time notifications for performance violations.
Flyte is an MLOps platform that supports maintenance, monitoring, tracking and automation of Kubernetes. It continuously monitors any model modifications and ensures that it is replicable. The tool helps maintain company compliance with any data updates. Flyte cleverly uses cash output to save time and money. It efficiently manages data preparation, model training, metric computing, and model validation.
ZenML is an excellent tool for comparing two experiments and for data transformation and evaluation. Additionally, it can be replicated using tracked automated tests, versioned data and code, and declarative pipeline setups. Open source machine learning applications allow for fast experiment iterations due to a cached pipeline. The tool has built-in assistants that compare and visualize results and parameters. It is also compatible with Jupiter notebooks.
Anaconda is a straightforward machine learning monitoring tool that has many useful features. The platform provides various useful libraries and Python versions. Pre-installation of any additional libraries and packages is available.
Note: We tried our best to feature the best tools/platforms available, but if we missed anything, then please feel free to reach out at [email protected]
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Consultant Intern: Currently in third year of B.Tech from Indian Institute of Technology (IIT), Goa. She is an ML enthusiast and has a keen interest in data science. She is a very good learner and tries to keep abreast with the latest developments in artificial intelligence.