Finally, we successfully developed a software as a service solution, which supports our customer’s media enterprises in the extraction of potentially relevant news content from social media.
The deep learning model, which has been implemented with Google TensorFlow and hosted in the Google Cloud forms the core of the project.
To evaluate the enormous amounts of data from social media almost in real-time using the deep learning algorithm, dynamic scalability has been used within the cloud and combined with Kubernetes.
Thanks to using Kubernetes and container technologies the intense training of the deep learning algorithm was completed in no time, paving the way for the introduction of continuous integration and a deployment pipeline. This way, we allowed our customer to train new deep learning models and to easily integrate them into the current system.
The solution developed by us, enables our customers to identify news topics, which can quickly gain relevance in the future, providing a competitive advantage in the creation of news services.
By using cloud technologies, initial capital costs were avoided. Costs merely incurred for the usage of cloud infrastructures (pay as you go). Therefore, our customers reached a return on investment within three months.