Starting with the dialogue helps to identify the imidate needs. But keeping it up and broaden the exchange will show us mid and long term opportunities and guide us to deeper business understanding and higher business value in return.
Opt for the best fitting solution for the client rather than the latest technology hype. Our goal is to provide a solution that is as adaptable as needed with minimal complexity. So it will work in the day to day business robust and reliable.
Define with the client, what would be valuable, and how to measure it. From there were deriving tasks and metrics to set the path for our data journey. During our data journey, we keep up the dialogue to ensure we're always on track. So we can address obstacles early and find proper solutions to make the most of our time and budget.
Business is innovatively changing. So our solution should adapt to it. To empower our data-driven solution to do so, we need to make our it aware of changes and give it the ability to work with feedback.
As Consultant and Data Scientist at SVA I developed various workshops on big data technologies and data science topics. Besides that I've implemented solutions data science solutions for customers and designed show cases for SVA. I adapted DevOp procedures to develop in a reproducable maner and deploy my work in a reliable and reproducable way. Besides my strong python knowlage I've learned to work with several big data and DevOp technologies like Kafka, ansible, MapR, Splunk and many more.
As Data Scientist at KI I developed a data driven software components for our customers as well as buld and maintained various data pipelines. Besides that I've ported one of our ussines solutions to in docker to make it flexible and portable for demos. In additon I've designed and implemented a system, with Keras and Tensorflow, for feature extraction from images with a human feedback loop.
As part of the core research and development team I aggregated data via scrapingand and performed various data and text mining tasks on them. Furthermore did research and imprementation of an recommender system prototype with an API. I also build up of an containerized Linux software stack, for greater flexebility and scalability.
As a private tutor I teached individual and small groups of students in analysis I + II, geometry I + II, ordinary differential equations on a regular bases and teached crash courses in these subjects. Besides that, I offered final thesis coaching and assistance for $\LaTeX$.
As a private tutor I teached individual and small groups of A level students in mathematics and physics in order to prepare them for regular and final exams. I also teached crash courses in these subjects.
Accelerate your anomaly detection pipeline from days to minutes by identifying bottlenecks and addressing them with appropriate architectural changes. Or why GPU's are faster than a compute cluster and hands-on example where Nvidia RAPIDS can help to speed up your preprocessing.
We talk about best practices from various predictive maintenance projects and demonstrate best practices and procedures to guide these projects to success. We'll talk about how we've helped clients to identify and prioritize use-cases, implemented them and how we build a feedback loop with the maintenance personnel on the shop floor.
We prove that the overpartition function $\overline{p}(n)$ is $\log$-concave for all $n \ge 2.$ The proof is based on Sills Rademacher type series for $\overline{p}(n)$ and inspired by DeSalvo and Pak’s proof for the partition function.
