Monitoring Tool Integrations

In our experience, nearly all AI development efforts, be they at large enterprises or new startups, begin by spending the first 3-6 months building their first ML pipelines from available tools. These custom integrations are time consuming and expensive to produce, can be fragile and frequently require drastic changes as project requirements evolve. 

Frequently, these custom ML pipelines only support a small set of built-in algorithms or a single ML library and are tied to each company’s infrastructure. Users cannot easily leverage new ML libraries, or share their work with a wider community.

Neuro facilitates adoption of robust, adaptable Machine Learning Operations (MLOps) by simplifying resource orchestration, automation and instrumentation at all steps of ML system construction, including integration, testing, deployment, monitoring and infrastructure management.

To maintain agility and avoid the pitfalls of technical debt, Neuro allows for the seamless connection of an ever-expanding universe of ML tools into your workflow.

We cover the entire ML lifecycle from Data Collection to Testing and Interpretation. All resources, processes and permissions are managed through our platform and can be installed and run on virtually any compute infrastructure, be it on-premise or in the cloud of your choice.


The various components of a machine learning workflow can be split up into independent, reusable, modular parts that can be pipelined together to create, test and deploy models.

Our toolset integrator, Toolbox, contains up to date out of the box integrations with a wide range of open-source and commercial tools required for modern ML/AI development.

For Monitoring, provides out of the box integrations with Algorithmia and also recommends and supports integration with the open source Prometheus + Grafana.


Algorithmia is a machine learning operations and management platform that manages all stages of the ML lifecycle

For monitoring, Algorithmia Insights, a feature of Algorithmia Enterprise, provides a metrics pipeline that can be used to instrument, measure, and monitor your ML models in production. It is used for ML model performance monitoring and provides inference and operational metrics for models to identify and correct model drift, data skews, and negative feedback loops. You can also set the tool to automatically trigger alerts and retraining jobs to mitigate model risk.

Insights also helps users analyze how their models are performing with real-world data by streaming model performance metrics into external monitoring systems, observability platforms, and application performance monitoring tools such as Datadog, Grafana, InfluxDB, New Relic, Kibana, and others.

Algorithmia can also be integrated with for managing deployment, and currently supports Java, Python, Rust, Ruby, R, JavaScript and Scala if you want to write your own model in the language of your choice using a different library.

Prometheus + Grafana

Prometheus is an open source application for application monitoring and alerts, and is typically used in conjunction with several other tools, such as an exporter to export local metrics. an alert manager to trigger alerts based on those metrics and a visualization tool to produce dashboards. It records real-time metrics in a time series database and allows for addition of labels such as information on the data source (which server the data is coming from) and other application-specific breakdown information, which can all be queried in real time.

Grafana is used in conjunction with Prometheus to produce web accessible charts, graphs, and alerts when connected to supported data sources (such as Prometheus alerts and monitoring data).