Marcel Raymond Steger

The scope of the project was Google Looker Studio with a BigQuery data source. The requirement was to create a flow diagram for displaying app usage, for which a Sankey Diagram appeared to be the most suitable solution. However, due to the limitation of existing Sankey Diagram implementations in Looker Studios Community Visualizations, (notably there was no coloring of the edges and recursive connections were prohibited), it was decided to develop a customer-tailored solution in the form of a Looker Studio Community Visualization. Another requirement was integrating a database from an AWS RDB Instance into BigQuery, located within a private VPN and behind an OpenVPN gate. To allow for synchronization of the data in BigQuery w.r.t this private AWS database, a solution was developed, which first connects via OpenVPN to the private AWS DB instance, upon connection queries the latest data, then connects to BQ, and finally appends it to the respective BigQuery tables. The Docker image was deployed as a Compute Engine instance and triggered via Cloud Scheduler, allowing for easy up-scaling in case of increasing memory requirements.

The requirement of the project was to calculate the sentiment of complex chat interactions between customers and service agents.  First, a classifier was trained, that distinguishes between simple chat interactions of customers and agents (e.g.: "I cannot login") and more complex ones (e.g.:  "The laws in my country have recently changed, which is why I want to revisit the terms of my membership, see Paragraph 1 Section 2a..."). Once that was in place, the sentiment of complex chat interactions was calculated for both users and agents using state-of-the-art Transformer language models. Subsequently, the sentiment scores were aggregated, using a smoothed moving average, and the results were visualized. Finally, the visualization was integrated into a dashboard to be used by the customer support managers for monitoring if chats get too negative and enabling them to intervene.

Ranging from feasibility study to PoC, to the pre-production of the final machine, the project encompassed multiple aspects of designing the computer vision system for a robotics machine.  Given an image capture of a 3D object and its 3D mesh data, an algorithm was developed, that segments the object from the background and calculates the affine rotation matrix that needs to be applied in order for the object in the real-world space to match the orientation of the object in the 3D mesh data. The requirements of this goal were: Additionally, to insure the stability of the vision system, extensive regression and unit tests were developed. 

The goal of the project was to replace an existing non-ml-based computer vision solution for an image classification problem, which has been shown to be susceptible to changes in lighting and other external factors. Because the result of this algorithm was directly used in machine logic and could lead to severe damage to the machine, the goal was 100% classification accuracy. The requirements of the project were determining the optimal conditions for image acquisition (camera position, exposure, and gain). Subsequently, the collection of data was undertaken over the course of months and over multiple machines in action. Next, a custom CNN architecture was chosen and trained on GPU on the images using image augmentation techniques, and regression tests were set in place. The resulting model was then converted to an ONNX format and deployed within in a C# application running directly on the machine.

The goal of the project was to research solutions for (semi) automated feature selection methods for Big Data use cases with highly distributed data in the automotive space. Given multiple company use cases (regression and classification), initial research was conducted towards state-of-the-art feature selection approaches, and statistical simulation studies have been carried out to assess the strengths and weaknesses of these approaches. The data sets consisted of both discrete and continuous features with highly correlated features as well as interactions in high-dimensional feature space. Both, classical filtering methods as well as stepwise methods based on machine learning approaches - such as regression and random forests - were among the researched methods. The most promising methods have then been implemented as Python modules in the context of the PySpark framework, following standard API design known from other scientific python packages such as sklearn.