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ENFINT MLOps Platform
What ENFINT MLOps Platform is
ENFINT MLOps Platform supports the development and operation of data science and machine learning projects across their lifecycle. It brings together managed Jupyter, Visual Studio Code, and RStudio environments, with IDE images that support Python, R, Java, and Scala. Its integrated MLflow component tracks experiments and registers models. Apache Airflow, connected with Git, orchestrates batch jobs, including model verification and deployment of complex batch models. For online services, Seldon Core provides deployment pipelines, including REST services built from models registered in MLflow. A Feast-based feature store manages data in historical storage and online databases, while monitoring draws on Evidently, Grafana, Prometheus, and Jaeger and can use configurable notification triggers. Projects group requirements, data sources, notebooks, and models, with access control lists, segregation of duties, and resource limits and quotas for project nodes. The platform uses a secured web interface for authentication and authorization. It can be deployed on-premise within enterprise infrastructure or on public cloud infrastructure, and supports online and offline stores and serving modes. ENFINT cites finance and banking, manufacturing, and marketing and retail projects as examples. Pricing is available on request; the product page offers a request-demo form. ENFINT says it is certified to ISO 27001:2022.
Who it suits
ENFINT MLOps Platform suits data science and machine learning teams that need development environments, experiment tracking, batch orchestration, model serving, feature storage, and monitoring in one platform. Its project controls and on-premise or public cloud deployment options may suit organizations in finance and banking, manufacturing, or marketing and retail.
What it does well
- Managed Jupyter, Visual Studio Code, and RStudio environments.
- MLflow experiment tracking and model registration.
- Airflow with Git integration orchestrates batch jobs.
- Seldon Core supports online model service deployment pipelines.
- Feast-based feature store supports historical and online data.
- Project access controls and node resource quotas.
What to know first
- Pricing is available only on request.
- A request demo is the stated route to discuss deployment.
- The listed platforms are self-hosted and web.
Plans
| Plan | Billed and limits | Price |
|---|---|---|
| Custom pricing | Request a demo · no public price or plan limits stated | On request |
As enfint.ai lists them, read 8 Oct 2026. The maker's own page has the final word.
Getting started
- Visit https://enfint.ai/enfintmlopsplatform.
- Choose on-premise deployment or public cloud infrastructure.
- Request a demo through the product page.
- Discuss custom pricing and deployment with ENFINT.
Verdict
Choose ENFINT MLOps Platform if your team needs a lifecycle platform combining development environments, orchestration, model serving, feature storage, monitoring, and project controls. Its on-premise and public cloud deployment options support different infrastructure arrangements. Look elsewhere if you need a publicly stated price or plan limits before contacting the provider.
Questions
What does ENFINT MLOps Platform cost?
Pricing is on request. The product page offers a request-demo form.
Where can the platform be deployed?
ENFINT lists on-premise deployment within enterprise infrastructure and deployment on public cloud infrastructure.
Which development environments and languages does it support?
It provides managed Jupyter, Visual Studio Code, and RStudio environments. Its IDE images describe support for Python, R, Java, and Scala.
Does it support online and offline data and serving?
The platform lists both an online and offline store, and both serving modes.
What access and resource controls are available?
Projects use segregation of duties and access control lists. The page also describes resource limits and quotas for project nodes.
What security certification does ENFINT state?
ENFINT says it is certified to ISO 27001:2022 and follows that standard to secure information it processes.
Facts from ENFINT MLOps Platform's own pages
- Access controls
- Projects support segregation of duties and access control lists, and the platform provides authentication and authorization through a secured web interface. · enfint.ai, 8 Oct 2026
- Batch workflows
- Apache Airflow with Git integration orchestrates batch jobs, including model verification and deployment of complex batch models. · enfint.ai, 8 Oct 2026
- Deployment
- The maker lists on-premise deployment within enterprise infrastructure and deployment on public cloud infrastructure, each with a request-demo call to action. · enfint.ai, 8 Oct 2026
- Development environments
- It provides managed development environments based on Jupyter, Visual Studio Code, and RStudio. · enfint.ai, 8 Oct 2026
- Experiment tracking
- Its integrated MLflow component tracks experiments and registers models using MLflow Model Registry. · enfint.ai, 8 Oct 2026
- Feature store
- The built-in feature store is based on Feast and supports managing data in historical storages and online databases. · enfint.ai, 8 Oct 2026
- Industries
- The page gives finance and banking, manufacturing, and marketing and retail as example industries and use cases. · enfint.ai, 8 Oct 2026
- Intended users
- ENFINT describes the platform as supporting data science and machine learning teams and cites finance and banking, manufacturing, and marketing and retail projects as examples. · enfint.ai, 8 Oct 2026
- Languages
- The platform describes support for Python, R, Java, and Scala in its IDE images. · enfint.ai, 8 Oct 2026
- Model serving
- Seldon Core provides deployment pipelines for online model services, including REST services based on models registered in MLflow. · enfint.ai, 8 Oct 2026
- Monitoring
- Monitoring capabilities are based on Evidently, Grafana, Prometheus, and Jaeger, with configurable triggers to notify users. · enfint.ai, 8 Oct 2026
- Project access controls
- Projects group requirements, data sources, notebooks, and models and use segregation of duties and access control lists. · enfint.ai, 8 Oct 2026
- Purpose
- The platform supports the full lifecycle of data science and machine learning project development and operations. · enfint.ai, 8 Oct 2026
- Resource controls
- The page describes resource limits and quota controls for project nodes. · enfint.ai, 8 Oct 2026
- Security certification
- The product page links to an ENFINT ISO 27001:2022 certification page. · enfint.ai, 8 Oct 2026
- Support information
- The product page provides a request-demo form, but does not state a support plan or support hours. · enfint.ai, 8 Oct 2026
- Workflow orchestration
- Apache Airflow with Git integration orchestrates batch jobs, model verification, and deployment of complex batch models. · enfint.ai, 8 Oct 2026
Where ENFINT MLOps Platform stands on Freedom251
- Feature Store Software#13 of 20