Products & services

Audit, develop and operationalize Machine Learning solutions

Too often, Machine Learning projects end up in disappointment and do not get to a production state or fail once they are there. With our approach and vision on ML we realize solutions that are here to stay, and truly change organizations and lives. We build lasting ML for scale-ups and SMEs where we can make an impact.

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Our approach

To realize lasting ML we work with a fixed approach. This approach consists of:

ML Audit

We always start with the ML audit; a 5-day on site review to assess the quality and potential of ML with a ML roadmap as key deliverable. We design the blueprint and create the business case before we start building your ML engine.

ML Development

We develop machine learning infrastructures and ML use cases. During development, testing and usage our Machine Learning Engineers make sure to involve the business experts.

ML Service

A model is not finished once live, it only starts when going to production. Enjins offers subscription based service for monitoring and logging of your ML infrastructure, making sure that failures are fixed when needed and model performance remains solid.

ML Audit

The ML audit is a 5-day on site review to assess the quality and potential of ML.

Key deliverables of the ML audit are:

  • Two days on site review with interviews, architecture scans and code reviews
  • Audit report including red flag summary
  • Deep dives on data quality and technical aspects
  • Deep dives on the business value of ML and possible use cases
  • ML roadmap and ML infrastructure blueprint

Swishfund | SnappCar | Finqle

ML Development

ML Engine
The MLE is tailor-made and completely built to optimally function in your use-case. The engine can make real-time predictions and connects for example to your website or database. This way, predictions and results can be used immediately in every process in your organization.

ML Infrastructure
We build a stable and future-proof data science infrastructure, to enable your (future) data scientists to put models in production.

Feedback loop

Machine learning does not exist without learning from the business experts. Therefore, our ML infrastructures typically include feedback loops that get all of the missing information out of the expert’s mind, greatly increasing model performance.

Vlucht-Vertraagd | Wonderkind | Berkman | Quin

ML Service

Enjins offers subscription based service for monitoring and support of your ML infrastructure:

  • Basic service: We monitor & control your ML infrastructure to guarantee uptime. Management of your data infrastructure, monitoring of all components and fixing bugs are also included.
  • Additional service: Be there to handle questions from your experts and optimise models over time, as well as supporting with implementing new models.

Quin | Goboony | Van Eeghen



Our product Deeploy enables rapid deployment of machine learning models to production. By easing this process and shortening the time to production the models are able to provide value quickly. Find out more at


A dashboard and other tools provide clear overview of the status and performance of the models. The status can be gauged in seconds, enabling quick and actionable insights.


We believe in opening the black-box of machine learning models by providing tools to explain the models’ behaviour. Increasing transparency leads to trust, a foundational pillar to create business value on top of machine learning.

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Get in touch

Curious to find out what Machine Learning can do for you?

Don’t hesitate to fill out the contact form and get in touch.

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De Entree 234
1101 EE Amsterdam

KvK: 71755101

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