Apache Submarine
Apache Submarine is an open-source platform for managing Machine Learning (ML) workflows on distributed computing infrastructure, including container-orchestrated and big data environments (machine learning platform).
- Unified platform for model development, training, and serving across heterogeneous compute backends (machine learning platform).
- Supports running ML workloads on container-based and big data clusters, including Hadoop and Kubernetes (distributed computing).
- Provides a workbench with notebook-style interfaces for data science and model experimentation (data science tooling).
- Includes job management and scheduling for distributed training and batch inference workloads (workflow orchestration).
- Integrates with existing data lakes and storage within Apache ecosystems such as Hadoop-based deployments (data platform integration).
More About Apache Submarine
Apache Submarine is an open-source ML platform (machine learning platform) under The Apache Software Foundation that focuses on enabling end-to-end ML workflows on existing distributed data and compute infrastructure. It is designed for environments where organizations run large-scale data processing and storage systems and want to execute ML development, training, and serving close to those data sources.
The project addresses use cases in which data teams need to coordinate collaborative model development, experiment tracking, and deployment while leveraging existing big data clusters and container orchestration frameworks (distributed computing). Submarine provides mechanisms to run ML jobs on resource managers already common in enterprise environments, such as Hadoop YARN or Kubernetes, enabling reuse of established capacity planning, security, and data locality configurations.
Core capabilities include a workbench interface that supports notebook-based workflows for data scientists and ML engineers (data science tooling). Within this environment, users can develop models, connect to underlying data sources, and prepare workloads for distributed training. Submarine coordinates submission and lifecycle management of training and inference jobs (workflow orchestration), abstracting away details of the underlying cluster engines while still allowing configuration of resource usage, container images, and runtime parameters.
Apache Submarine fits into enterprise data platforms that already rely on Apache Hadoop and related projects (data platform integration). By operating within these ecosystems, it can access data stored in HDFS or similar storage layers and use existing authentication, authorization, and governance policies. Submarine can also run on Kubernetes clusters, giving organizations deployment flexibility between on-premises (on-prem) and cloud-based environments, while maintaining a single logical interface for ML job management.
From an architectural perspective, Submarine typically includes server components, RESTful APIs, and integration points with underlying schedulers (platform services). Its design allows multiple users to submit and monitor jobs, manage experiments, and access shared environments. This supports team-based workflows where data scientists, ML engineers, and platform operators collaborate using one ML control plane rather than disparate, siloed tools.
In the context of an enterprise technology directory, Apache Submarine can be categorized as a ML platform for workload orchestration on big data and container environments, with capabilities in data science tooling, resource management, and integration with Apache-centric data platforms. It is relevant for organizations that prefer to run ML workloads within their existing Apache-based stacks or Kubernetes clusters and need coordinated management of training and serving pipelines.