CI/CD for Data Pipelines: A Game-Changer with AnalyticsCreator
Continuous Integration and Continuous Delivery (CI/CD) for Data Pipelines: It is a Game-Changer with AnalyticsCreator.
Benjamin Aunkofer is Lead Data Scientist at DATANOMIQ, a consulting company for applied data science in Berlin. He is lecturer for Data Science and Data Strategy at HTW Berlin and gives trainings for Business Intelligence, Data Science and Machine Learning for companies.
Continuous Integration and Continuous Delivery (CI/CD) for Data Pipelines: It is a Game-Changer with AnalyticsCreator.
Object-centric Data Modelling for Process Mining and other Applications on the Cloud Data Mesh Architecture
Infrastructure as Code significantly enhances the development and maintenance of Cloud-based Data Infrastructures.
Data Mesh on Azure Cloud for Business Intelligence and Data Science and Process Mining Applications
Lambda Architecture vs Kappa Architecture for Big Data Cloud Platforms? Let us discuss which architecture suits best for what use cases.
Scalable Cloud Data Platform for Shopfloor Management
How to speed up claims processing with automated car damage detection and cost estimation for car insurances. Process optimization with AI!
completely independent of the tools, there is a very general procedure in this data-driven process analysis you should understand and which we would like to describe with the following infographic
In deep learning, there are different training methods. Which one we use in an AI project depends on the data provided by our customer: how much data is there, is it labeled or unlabeled? Or is there both labeled and unlabeled data?
Deep Learning helps companies to automate operative processes in many areas. Industrial companies in particular also benefit from product quality assurance. Computer Vision enables automation to identify scratches and cracks on product item surfaces.