Best Machine Learning Software for Amazon CloudWatch

Compare the Top Machine Learning Software that integrates with Amazon CloudWatch as of October 2025

Sort By:

Amazon CloudWatch Machine Learning Clear Filters

This a list of Machine Learning software that integrates with Amazon CloudWatch. Use the filters on the left to add additional filters for products that have integrations with Amazon CloudWatch. View the products that work with Amazon CloudWatch in the table below.

What is Machine Learning Software for Amazon CloudWatch?

Machine learning software enables developers and data scientists to build, train, and deploy models that can learn from data and make predictions or decisions without being explicitly programmed. These tools provide frameworks and algorithms for tasks such as classification, regression, clustering, and natural language processing. They often come with features like data preprocessing, model evaluation, and hyperparameter tuning, which help optimize the performance of machine learning models. With the ability to analyze large datasets and uncover patterns, machine learning software is widely used in industries like healthcare, finance, marketing, and autonomous systems. Overall, this software empowers organizations to leverage data for smarter decision-making and automation. Compare and read user reviews of the best Machine Learning software for Amazon CloudWatch currently available using the table below. This list is updated regularly.

1

InsightFinder

InsightFinder

InsightFinder Unified Intelligence Engine (UIE) platform provides human-centered AI solutions for identifying incident root causes, and predicting and preventing production incidents. Powered by patented self-tuning unsupervised machine learning, InsightFinder continuously learns from metric time series, logs, traces, and triage threads from SREs and DevOps Engineers to bubble up root causes and predict incidents from the source. Companies of all sizes have embraced the platform and seen that business-impacting incidents can be predicted hours ahead with clearly pinpointed root causes. Survey a comprehensive overview of your IT Ops ecosystem, including patterns, trends, and team activities. Also view calculations that demonstrate overall downtime savings, cost of labor savings, and number of incidents resolved.

Starting Price: $2.5 per core per month

View Software
2

Amazon Lookout for Metrics

Amazon

Reduce false positives and use machine learning (ML) to accurately detect anomalies in business metrics. Diagnose the root cause of anomalies by grouping related outliers together. Summarize root causes and rank them by severity. Seamlessly integrate AWS databases, storage services, and third-party SaaS applications to monitor metrics and detect anomalies. Automate customized alerts and actions when anomalies are detected. Automatically detect anomalies within metrics and identify their root causes. Lookout for Metrics uses ML to detect and diagnose anomalies within business and operational data. Detecting unexpected anomalies is challenging since traditional methods are manual and error-prone. Lookout for Metrics uses ML to detect and diagnose errors within your data, with no artificial intelligence (AI) expertise required. Identify unusual variances in subscriptions, conversion rates, and revenue, so you can stay on top of sudden changes.

View Software
3

TruEra

TruEra

A machine learning monitoring solution that helps you easily oversee and troubleshoot high model volumes. With explainability accuracy that’s unparalleled and unique analyses that are not available anywhere else, data scientists avoid false alarms and dead ends, addressing critical problems quickly and effectively. Your machine learning models stay optimized, so that your business is optimized. TruEra’s solution is based on an explainability engine that, due to years of dedicated research and development, is significantly more accurate than current tools. TruEra’s enterprise-class AI explainability technology is without peer. The core diagnostic engine is based on six years of research at Carnegie Mellon University and dramatically outperforms competitors. The platform quickly performs sophisticated sensitivity analysis that enables data scientists, business users, and risk and compliance teams to understand exactly how and why a model makes predictions.

View Software
4

Amazon SageMaker Debugger

Amazon

Optimize ML models by capturing training metrics in real-time and sending alerts when anomalies are detected. Automatically stop training processes when the desired accuracy is achieved to reduce the time and cost of training ML models. Automatically profile and monitor system resource utilization and send alerts when resource bottlenecks are identified to continuously improve resource utilization. Amazon SageMaker Debugger can reduce troubleshooting during training from days to minutes by automatically detecting and alerting you to remediate common training errors such as gradient values becoming too large or too small. Alerts can be viewed in Amazon SageMaker Studio or configured through Amazon CloudWatch. Additionally, the SageMaker Debugger SDK enables you to automatically detect new classes of model-specific errors such as data sampling, hyperparameter values, and out-of-bound values.

View Software