Embed Machine Learning predictions anywhere

Run predictive models close to your data as SQL or JavaScript, eliminating costly integrations and simplifying your application infrastructure.

  • In-Database inference: PostgreSQL, MySQL, MariaDB and SQLite
  • In-App inference: Firebase and Javascript

With our Free tier, you get unrestricted access to MLPloy and all its functionality. No registration required.

Seamless deployment

Embed ML models in seconds straight into your database, backend, app or BI dashboards

Cheaper infrastructure

No need for additional infrastructure and costly ML experts, use at best your existing resources

Secure predictions

Make ML predictions on your infrastructure, minimize the attack surface and simplify GDPR compliance

75% reduction in costs and complexity*

* It costs you more than 10.000,00 EUR every year to deploy and maintain an ML model (ML engineers & devops, infrastructure). With MLPloy, it costs you 2.400,00 EUR yearly (no integration costs, no additional infrastructure). For simple projects, the free tier costs you zero.

How does it work?

1. Data Scientist

Upload ML models, evaluation metrics and tests using the MLPloy REST API & Python API client.

2. Head of Data / Head of BI

Assess progress and review metrics using the MLPloy dashboard. Enable your team to deliver ML predictions to either dashboards or products.

3. Data Analyst / Product Developer

Copy the ML model SQL query or Javascript code from the MLPloy dashboard and paste it straight into your dashboard or app.

4. Product Manager

Add ML predictions to your app without worrying about GDPR, security and dependence on external APIs for scoring.

Head of BI

“It makes a complex process that involves multiple stakeholders super easy.”

Data Analyst

“I deployed a Risk Prediction model on our internal dashboard in literally seconds. That’s crazy.”

Freelance Data Scientist

“My clients love it, it really helps a lot with communication and handover.”

Contact

Happy to chat and help you simplify the deployment of ML models. You can reach us at hello@mlploy.com, contact form, chat and Slack.

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