As professional data scientists, you know what tools and technologies work best for what you are trying to accomplish, from prototyping ML-based pipelines to deploying scalable AI services across the enterprise. You also know where you don’t want to waste time. Think data access, data pre-processing, feature engineering, or model training and testing.
That’s why Dataiku’s transparent work environment is designed to bring flexibility to every step of the process, from raw data to automated service.
Automate data-preprocessing, feature engineering, and model training to quickly discover and build the ML and AI services you need to deliver.
Save time with parallel model building and autoML functionalities - see the performance of hundreds of models competing in real-time, and quickly identify the obvious winners (and losers).
Create a one-stop-shop in a visual interface to load models and serve requests in just a few clicks. Bye-bye human bottlenecks. Hello quick models to deployment and user independence.
In Dataiku, rest assured that any code you deploy is reproducible and that even unmaintained projects won’t fall through the cracks and fail.
Create custom reusable components (plugins) out of your existing code and leverage the tools (R, Python, Scala, Pig…) and libraries (MLlib, H2O, XGboost, Scikit Learn, TensorFlow..) from the rich and ever-growing open-source ecosystem to build & run data services from scratch.