Best Machine Learning Software for AWS Lambda
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Compare the Top Machine Learning Software that integrates with AWS Lambda as of October 2025
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This a list of Machine Learning software that integrates with AWS Lambda. Use the filters on the left to add additional filters for products that have integrations with AWS Lambda. View the products that work with AWS Lambda in the table below.
What is Machine Learning Software for AWS Lambda?
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 AWS Lambda currently available using the table below. This list is updated regularly.
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1
Amazon CodeGuru
Amazon
Amazon CodeGuru is a developer tool powered by machine learning that provides intelligent recommendations for improving code quality and identifying an application’s most expensive lines of code. Integrate Amazon CodeGuru into your existing software development workflow where you will experience built-in code reviews to detect and optimize the expensive lines of code to reduce costs. Amazon CodeGuru Profiler helps developers find an application’s most expensive lines of code along with specific visualizations and recommendations on how to improve code to save money. Amazon CodeGuru Reviewer uses machine learning to identify critical issues and hard-to-find bugs during application development to improve code quality. -
2
BentoML
BentoML
Serve your ML model in any cloud in minutes. Unified model packaging format enabling both online and offline serving on any platform. 100x the throughput of your regular flask-based model server, thanks to our advanced micro-batching mechanism. Deliver high-quality prediction services that speak the DevOps language and integrate perfectly with common infrastructure tools. Unified format for deployment. High-performance model serving. DevOps best practices baked in. The service uses the BERT model trained with the TensorFlow framework to predict movie reviews' sentiment. DevOps-free BentoML workflow, from prediction service registry, deployment automation, to endpoint monitoring, all configured automatically for your team. A solid foundation for running serious ML workloads in production. Keep all your team's models, deployments, and changes highly visible and control access via SSO, RBAC, client authentication, and auditing logs.Starting Price: Free -
3
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 -
4
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. -
5
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.
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