Machine Learning Model Monitoring is the operational stage that follows model deployment in the machine learning lifecycle. It comprises keeping an eye out for changes in the ML models, such as model deterioration, data drift, and idea drift, and ensuring that the model is still performing well. Many model monitoring software tools are available to monitor these models’ changes. Let us look at some of the most helpful ML model monitoring tools.
Neptune AI is an MLOps company designed for research and production teams who run a large number of experiments. It can arrange training and production metadata as per given preferences 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 shown using Neptune.
Arise AI is a tool for monitoring ML models that can improve a project’s observability and assist users in troubleshooting production AI. It also enables ML engineers to upgrade current models robustly. Additionally, it provides a Pre-launch validation toolbox that can run pre- and post-launch validation checks and acquire confidence in the model’s performance. Additionally, it offers automated model monitoring and simple integration.
WhyLabs is a model observability and monitoring tool that aids ML teams in keeping track of data pipelines and ML applications. It aids in detecting data bias, data drift, and data quality degradation. It does away with the necessity for manual problem-solving, saving time and money in the process. Regardless of scale, this tool may be used to work with both structured and unstructured data.
Qualdo is a tool for tracking the performance of machine learning models in Google, AWS, and Azure. Users can track the progress of their models throughout their lifecycles using Qualdo. Qualdo allows users to acquire insights from production ML input/predictions data, logs, and application data to monitor and enhance your model’s performance. It also uses Tensorflow’s data validation and model assessment capabilities and provides tools for tracking the performance of the ML pipeline in Tensorflow.
Fiddler is a model monitoring tool with an intuitive, uncomplicated UI. It enables users to manage complex machine learning models and datasets, deploy machine learning models at scale, explain and debug model predictions, examine model behavior for complete data and slices, and monitor model performance. It provides users with basic information about how well their ML service functions in production. Fiddler users can also establish alerts for a model or collection of models in a project to inform them of production issues.
Seldon Core is an open-source platform to implement machine learning models on Kubernetes. It is framework independent, works on any cloud or on-premises, and supports the best machine-learning toolkits, libraries, and languages. Additionally, it transforms your machine learning models (ML models) or language wrappers (Java, Python) into production REST/GRPC microservices. Thousands of production machine learning models may be packaged, deployed, tracked, and managed using this MLOps platform.
Anodot is an AI monitoring tool that automatically comprehends the data. The program is designed from the ground up to ensure that it interprets, analyzes, and correlates the data to improve the operation of any business. It monitors several things simultaneously, including revenue, partners, and Telco networking.
Evidently is an open-source ML model monitoring system. It aids in analyzing machine learning models during their design, validation, or production monitoring. A pandas DataFrame is used by the tool to produce interactive reports. It assists in assessing, testing, and tracking the effectiveness of ML models from validation to production. Evidently contains monitors that gather information from a deployed ML service, including model metrics. It can be used to create dashboards for real-time monitoring.
With Censius, an AI model observability platform, users can track the entire ML pipeline, decode forecasts, and proactively address problems for a better business outcome. Using Censius Monitors, it automates continuous model monitoring for concerns with performance, drift, outliers, and data quality. Additionally, customers can receive real-time notifications for performance violations.
Flyte is an MLOps platform that aids in the maintenance, monitoring, tracking, and automation of Kubernetes. It continuously monitors any model modifications and ensures that it is replicable. The tool aids in maintaining the company’s compliance with any data updates. Flyte cleverly uses the cached output to save time and money. It expertly manages data preparation, model training, metric computing, and model validation.
ZenML is an excellent tool for the comparison of two experiments and for transforming and assessing data. Additionally, it may be replicated using automated trials that are tracked, versioned data and code, and declarative pipeline setups. The open-source machine learning application allows for Fast experiment iterations due to the cached pipeline. The tool features built-in assistants that compare and visualize results and parameters. It is also compatible with the Jupyter notebook.
Anaconda is a straightforward machine learning monitoring tool that has numerous helpful features. The platform provides a variety of 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 Asif@marktechpost.com
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Consultant Intern: Currently in her 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 be well versed with the latest developments in Artificial Intelligence.