Data science

We combine data science, software engineering and business understanding to deliver systems that solve real problems. We call it end-to-end machine learning. Get in touch to schedule a workshop on how to get started.

Valuable data?

We help you get started

Do you have a lot of data, but doesn’t know how to put it to use? How can you make it valuable to your business? What are the use-cases and what will be required to make it happen?

We help you get started in three simple steps:

  • A workshop with no strings attached to learn about your business and help find the valuable use-cases
  • An assessment of where you currently are. What prerequisites for creating value from your data are in place? What is a reasonable ambition for the next 2-3 months?
  • A hand-on project to help get the first use-case from idea to value. At the same time we help shape the strategy for your future data journey.

Get in touch to learn more

Machine learning in the cloud

With our toolbox for deployment

Deepinsight can provide a premade configuration with great open source tools for cloud deployment. This enables you to have machine learning in production very, very fast.

The only requirement is having access to deploy Kubernetes clusters. This is easily available on public clouds such as AWS, Google Cloud and Azure, usually done in about 5 minutes. Kubernetes can also be deployed on premise.

The last mile of data science doesn't take forever

A common trend in today’s corporate world is that many machine learning models with great business impact never make it to production. In silos, analysts create the models, the business side evaluates them and IT deploys them. Heavy handovers can take years and can be expensive.

Typical drivers of costs are:

  • Heavy handovers on understanding requirements
  • Costly fights on prioritization
  • Having to translate data science code to “production code”

These problems might appear organizational, but maybe they aren’t?

What if we could find a compromise where data science code is deployable, testable and scalable?

Flexibility matters. Kubernetes and open source tools gives balance between flexibility and access to out-of-the-box options that enables rapid deployment. Packaging code in the right way enables us to push prototypes to production immediately, and show those results in dashboards or APIs.

machine learning, data science, kubernetes

Data science is more fun when it has impact

In 2019, Deepinsight worked on a very interesting consulting project with significant end-to-end responsibility. Our client had several open analytical problems and a product manager who only believed in end-to-end results.

To solve this, we started to deploy our models to Kubernetes. Results from our algorithms were highly visible, also in early prototypes of our work, so of course testing was important from the very beginning.

Continuous integration and deployment paid off

Some very interesting patterns emerged after a few months.

  • With better testing, we deployed changes to production a few times a week.
  • Frequent deployments enabled experimental feature to be deployed to Kubernetes as part of the “definition of done”.
  • Average time from user story to production dropped from several weeks to around 16-40 working hours.
  • With early deployments, our models were up and running for a few weeks before used by the client. This increased our confidence that our models would work as expected.
  • Seeing results early gave us faster feedback loops and enabled us to make improvements based on data.

The right technology was however crucial in achieving this tempo. For 2020 we will improve on what we learned from last year. Get in touch if you would like to learn more about how we enable companies deliver better services.

data science, machine learning, engineering
Contact us on No lock-in and as part of a consulting project we will give you all rights to the code (except resell).
data science, software engineering
Getting started with

Cloud platforms

We have a lot of experience with cloud platforms, and can help you get started on your cloud journey.

Perhaps you have some existing machine learning pipelines and want to make them run faster and more effectively on a cloud platform. We can help!

We can:

  • help you move your data and pipelines securely to Google Cloud or AWS
  • deploy models to a production environment running in the cloud
  • help you upgrade research level code to production quality
  • provide advice on monitoring model quality and pipeline reliability
  • provide advice on latency and costs