Datadog monitors cloud applications, infrastructure, and performance, including AWS and Azure. It offers dashboards, log aggregation, alerting, and error detection. Users value application performance monitoring, log management, and root cause analysis. Desired improvements include better tool integration, intuitive interfaces, expanded metrics, and enhanced alerts, with concerns about pricing and UI slowness.
It has always scaled for us. Cost scales up too, but that is not necessarily a bad thing. It's reasonable for what they're providing.
It costs the same amount it would if we were hosting it ourselves, so we are incredibly happy with the cost.
It has always scaled for us. Cost scales up too, but that is not necessarily a bad thing. It's reasonable for what they're providing.
It costs the same amount it would if we were hosting it ourselves, so we are incredibly happy with the cost.
Generate interactive reports in the notebook or export them as an HTML file. Use them for visual evaluation, debugging and sharing with the team. Run the data and model checks as part of the pipeline. Integrate with tools like Mlflow or Airflow to schedule the tests and log the results. Collect the model quality metrics from the deployed ML service. Currently works through integration with Prometheus and Grafana.
Model Performance Management (MPM) is the foundation of model/MLOps, providing continuous visibility into your training and production ML, understanding why predictions are made, and enabling teams with actionable insights to refine and react to changes to improve your models. MPM is reliant not only on metrics but also on how well a model can be explained when something eventually goes wrong.
Enable observability to detect data and ML issues faster, deliver continuous improvements, and avoid costly incidents.
NannyML empowers data scientists to detect and understand silent model failure, so you can end these worries in minutes!
NannyML turns the machine learning flow into a cycle, empowering data scientists to do meaningful and informed post-deployment data science to monitor and improve models in production through iterative deployments.
Galileo is used for inventory management, ensuring real-time tracking of stock levels. Users appreciate its integration with existing systems, automation of tasks, and data accuracy. Galileo offers detailed analytics, streamlines workflows, and reduces manual errors. Users also value its data visualization, intuitive design, dashboards, real-time insights, and customizable features. Some users suggest improvements in integration, customer support, and documentation.
Arthur helps data scientists, ML engineers, product owners, and business leaders accelerate model operations at scale. We work with enterprise teams to monitor, measure, and optimize model performance and quality.
Systematic, automated testing with the test harness. Comprehensive analytics.
Monitoring and ML observability that gets to the root cause, for faster debugging.
Best-in-class explainability accuracy. Demonstrate model quality and fairness.
