Operational excellence in data science

Photo by Phil Botha on Unsplash


Operational excellence for data science teams

Data science is a fast growing domain. As the ecosystem of software frameworks and tools is developing at a fast pace, it is hard to keep up. In contrast to software development, no standard workflow has been established yet. Most companies focus on building the right thing and less on building the thing right. A lot of data science projects fail, not because of missing data science know how, but because of a lack of a best practice workflow.

We introduce a framework that facilitates the collaboration within the data science team, ensures documentation and reproducibility of results, and makes the deployment of AI models child’s play.

Solution engineering on the Google Cloud Platform

Data-driven products require more than a well trained AI model. They consist of a whole pipeline from data ingestion to an output that generates insights or triggers actions. We have acquired a lot of experience in building end-to-end solutions on the Google Cloud Platform. Solutions that require low to no operations, scale without limits, and leverage the best of Google’s cutting edge technology.

Together with our strategic partner Panter, we deliver everything from the architecture to the implementation.

Video analytics

We have developed a solution for brand detection in sports videos or live broadcasts as an MVP, allowing to determine the effective screen time of the sponsors.

The serverless cloud architecture behind this solution can analyse hundreds of video frames per second and is applicable to a wide range of use cases that require video analytics.

Explore the MVP

Flash flood prediction

We are developing an AI powered early warning system for flash floods that predicts an increasing flood risk as a consequence of heavy rain and thunderstorms.

Our vision is that regions with a high exposure to flash flood risks around the world have a “flash flood watchdog” that alerts emergency forces and the population of imminent danger and helps protecting communities and saving lives.

Car claims automation

We were advisors to an insurance company developing an automated car claims settlement solution that estimates repair costs based on their customers’ pictures of a damaged car.

We assisted the data science team in the data exploration and model training phase on the Google Cloud Platform (GCP) and proposed an architecture on the GCP for the deployment.

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IoT sensor-to-analytics pipeline

With our IoT sensor-to-analytics pipeline, your IoT sensor data are visualised and analysed easily, giving you instant insights.

Using IoT Core, Cloud Bigtable, and BigQuery, we built a highly scalable backend that can handle any number of devices and any type of sensor for any kind of use case.

Lab analytics dashboard

For a consultancy firm in the pharmaceutical industry, we built the analytics backend of a web based dashboard for process optimisation in quality control labs.

We implemented a completely serverless solution for data processing, KPI calculation, and online analytics using Dataflow, Cloud Functions, and BigQuery.

Photo by Louis Reed on Unsplash


Developing data-driven products requires state of the art tools and a high-end infrastructure.

Google Cloud Platform

As a cloud-native company, we have been using the Google Cloud Platform (GCP) from the beginning. We are convinced that it has the best offering when it comes to data analytics and machine learning. We build on this cutting edge infrastructure on our own projects and when delivering our services to our clients.

AI and Machine Learning

With TensorFlow, Google's AI Hub, and the AI Platform on the GCP, we use the best in class resources and infrastructure to develop and run our prediction models. In addition to that, we use Kubeflow to cover the whole data science workflow of exploring, training at scale, and deploying.


Whenever possible, we build a modular cloud architecture with components that can be developed, run, and scaled independently. Also, we are eager adopters of the serverless paradigm.

Read more about the technology we use

About Us

We engage with data science teams in the exploration, development, and distribution of AI products and services. To empower corporate data science teams, we focus on establishing operational excellence and best practice workflows using the AI Platform on the Google Cloud Platform (GCP) as well as Kubeflow. Together with our strategic partner Panter, we provide services from engineering cloud architectures around AI models to building end-to-end solutions on the GCP.

The Team

Antonio Oro

Founder & CEO

Juri Sarbach

Founder & CTO

Google Cloud Certified Professional Data Engineer, Google Cloud Certified Associate Cloud Engineer.

Kate Gadola

Software Developer

Peter Giger

Model Developer



Quantworks AG
c/o The Hub Zurich Association
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CH-8005 Zürich
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