Our products and services help to solve your data science challenges

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 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.

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Engine Control

Deploy

Our product 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.

Monitor

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.

Explain

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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