ENFINT MLOps Platform
ENFINT MLOps Platform supports the development and operation of data science and machine learning projects across their lifecycle.
What ENFINT MLOps Platform is
ENFINT MLOps Platform supports the development and operation of data science and machine learning projects across their lifecycle. Managed development environments are based on Jupyter, Visual Studio Code, and RStudio, with IDE images supporting Python, R, Java, and Scala. Integrated MLflow tracks experiments and registers models through MLflow Model Registry. Apache Airflow with Git integration orchestrates batch work, including model verification and deployment of complex batch models. Seldon Core supports online model-serving pipelines, including REST services based on models registered in MLflow. A Feast-based feature store manages data in historical storage and online databases. Monitoring uses Evidently, Grafana, Prometheus, and Jaeger, with configurable notification triggers. Projects group requirements, data sources, notebooks, and models, and support segregation of duties, access control lists, and resource quotas. The secured web interface provides authentication and authorization. ENFINT lists on-premise deployment within enterprise infrastructure and deployment on public cloud infrastructure. The platform is paid, with pricing available on request; the product page offers a demo request but does not state support plans or hours.
Good
- Managed Jupyter, VS Code, and RStudio environments.
- Integrated MLflow experiment tracking and model registry.
- Supports online and offline stores.
- Offers on-premise and public cloud deployment.
Watch out
- No public price or plan limits stated.
Who it suits
It suits data science and machine learning teams that need project environments, model workflows, and monitoring. ENFINT cites finance and banking, manufacturing, and marketing and retail as example industries.
Verdict
The platform combines development environments with batch orchestration, serving, feature storage, and monitoring. Teams should request a demo to discuss deployment and pricing for their setup.
Plans and prices
From ENFINT MLOps Platform's own pricing page, checked 8 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| Custom pricing | Priced on request | Request a demo · no public price or plan limits stated |
The facts
Each one read from ENFINT MLOps Platform's own pages, with the date it was checked.
What it does
- Access controls
- Projects support segregation of duties and access control lists, and the platform provides authentication and authorization through a secured web interface.checked 8 Oct 2026
- Batch workflows
- Apache Airflow with Git integration orchestrates batch jobs, including model verification and deployment of complex batch models.checked 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.checked 8 Oct 2026
- Development environments
- It provides managed development environments based on Jupyter, Visual Studio Code, and RStudio.checked 8 Oct 2026
- Experiment tracking
- Its integrated MLflow component tracks experiments and registers models using MLflow Model Registry.checked 8 Oct 2026
- Feature store
- The built-in feature store is based on Feast and supports managing data in historical storages and online databases.checked 8 Oct 2026
- Industries
- The page gives finance and banking, manufacturing, and marketing and retail as example industries and use cases.checked 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.checked 8 Oct 2026
- Languages
- The platform describes support for Python, R, Java, and Scala in its IDE images.checked 8 Oct 2026
- Model serving
- Seldon Core provides deployment pipelines for online model services, including REST services based on models registered in MLflow.checked 8 Oct 2026
- Monitoring
- Monitoring capabilities are based on Evidently, Grafana, Prometheus, and Jaeger, with configurable triggers to notify users.checked 8 Oct 2026
- Project access controls
- Projects group requirements, data sources, notebooks, and models and use segregation of duties and access control lists.checked 8 Oct 2026
- Purpose
- The platform supports the full lifecycle of data science and machine learning project development and operations.checked 8 Oct 2026
- Resource controls
- The page describes resource limits and quota controls for project nodes.checked 8 Oct 2026
- Security certification
- The product page links to an ENFINT ISO 27001:2022 certification page.checked 8 Oct 2026
- Support information
- The product page provides a request-demo form, but does not state a support plan or support hours.checked 8 Oct 2026
- Workflow orchestration
- Apache Airflow with Git integration orchestrates batch jobs, model verification, and deployment of complex batch models.checked 8 Oct 2026
