Datadog is a monitoring and analytics platform for cloud-scale application infrastructure. Combining metrics from servers, databases, and applications, Datadog delivers sophisticated, actionable alerts, and provides real-time visibility of your entire infrastructure. Datadog includes 100+ vendor-supported, prebuilt integrations and monitors hundreds of thousands of hosts.
Algorithmia might be a bit more popular than Datadog. We know about 5 links to it since March 2021 and only 5 links to Datadog. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Ideally, if we had access to the underlying infrastructure, we could probably install the Datadog Agent and configure it to send our logs directly to Datadog, or even use AWS Lambda functions or Azure Event Hub + Azure Functions in case we were facing some specific cloud scenarios. - Source: dev.to / 8 months ago
Currently supported : Datadog, Jenkins, DNS, HTTP. Source: over 1 year ago
Datadog is a powerful monitoring and security platform that gives you visibility into end-to-end traces, application metrics, logs, and infrastructure. While Datadog has great documentation on their Kubernetes integration, we've observed that there's some missed nuance that leads to common pitfalls. - Source: dev.to / almost 3 years ago
.. Is to see you email address being silently distributed to every single company that I've watched a talk from. And now suddenly get several promotional spam emails per day from some 4-5 different domains like instana.com, datadoghq.com, snyk.io, cockroachlabs.com (some of them send even multiple emails per day!). Source: almost 3 years ago
We're commonly doing this with logging, using services such as Loggly or DataDog. We're using managed databases, be it on AWS, Heroku or database-vendor-specific solutions. We're storing binaries on S3. Externalising user authentication and authorization might be a good candidate as well. - Source: dev.to / about 3 years ago
To push a model into production, there are additional concerns which the tools in the versioning, deployment and release space aim to solve. This includes obtaining adequate infrastructure to run the model reliably and facilitating easy model release or rollback. Solutions in the MLOps space includes Kubeflow, Pachyderm and Algorithmia. - Source: dev.to / over 2 years ago
And for enterprises that want to do the same with ML you can use algorithmia.com. Source: over 2 years ago
Algorithmia advertises themselves as an MLops platform for data scientists, and they provide an easy way to host models on a scalable REST API. Source: over 2 years ago
Seems similar to https://algorithmia.com. Source: over 2 years ago
Algorithmia.com — Host algorithms for free. Includes free monthly allowance for running algorithms. Now with CLI support. - Source: dev.to / almost 3 years ago
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