Data science is more art than science. Successful projects build on top of fast prototyping in a flexible environment.
At cognaize, we have built a versatile environment, integrating Big Data, computational power, and microservices into one platform.
In order to try out ideas, data scientists need a versatile and powerful environment. At cognaize, we customized Apache Zeppelin and integrated secure access our Big Data library for training data, linking it to scalable computational power and providing easy access to our microservices.
We have built an internal library of all datasets used for training any of our models. This allows for reproducibility and, better explainability of machine predictions. However, it also creates the challenge of scalability and acceptable performance. Machine Learning profits from large datasets. At cognaize, we are deploying Big Data solutions to horizontally distribute brute force manipulations. Allowing for linear scalability is enabling us to keep processing time constant by adding more resources for growing datasets.
Training of Artificial Intelligence models requires considerable computational power. At cognaize, we use hardware optimized parallelization. On top of our own hardware, we have configured our systems to allow seamless integration with cloud resources as necessary.
All microservices are fully integrated with our data science platform. Reusing tested services allow for even faster experimentation with ideas and prototyping.
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