Dataiku combines the simplicity of AutoML for fast prototyping with more advanced visual ML capabilities for creating sophisticated models — fast.
From prediction, clustering, and time series forecasting to causal ML and computer vision, data scientists and analysts alike can build and compare production-ready models quickly and with white-box explainability.
Dive Deeper on Dataiku AutoML Features
Advanced data scientists can programmatically develop custom models (using Python, R, and other languages) or import models developed with MLFlow.
To ensure external models are visible and interpretable to the rest of the team, Dataiku captures the details of MLFlow experiments or CloudML models and automatically provides model comparisons and explainability reports.
Broadly, we love Dataiku. We do have a mix of people that go more toward AutoML and visual tools as well as one data scientist who loves to work in code.
But that’s the beauty of Dataiku and why we chose it … the team has the flexibility: If they want to nerd out and go under the hood, they can do that. If they need a quick model, they can do that too.
Ben Powis
Ayca Kandur
Jennifer Garcia
Dataiku’s familiar model design, deployment, and governance experience makes it easy to include deep learning as part of data projects and business applications.
Define custom deep learning architectures with Keras and Tensorflow, or take advantage of pretrained models, transfer learning, and no-code interfaces for computer vision tasks such as image classification and object detection.
Both technical and non-technical users can better understand the outcomes of ML models thanks to robust explainability features in Dataiku.
An extensive battery of interactive performance and interpretation reports includes fairness analysis, what-if analysis, stress tests, and more.
With Dataiku, you have all the tools to not only build trusted, high-quality ML models, but deploy, monitor, and manage them as well.
From model comparison to drift detection, model retraining, and so much more, Dataiku allows you to truly deploy ML at scale.
Learn More About MLOps With Dataiku
For large computation or model training jobs, Dataiku allows you to automatically and efficiently scale workloads with on-demand, elastic resources powered by Spark and Kubernetes on your cloud of choice.
Pre-configured and fully managed clusters abstract away the complexity of containerized infrastructure, so you spend more time doing what you love and less time setting up backend resources.
READ THE PUSHDOWN COMPUTATION BLOG POSTThe power of Dataiku, the Universal AI Platform, is the ability to build traditional ML as well as generative AI-based applications — all in one place. Builder tools for generative AI in Dataiku include:
Prologis democratized AI with Dataiku, leveraging AutoML features to enable more than 2,000 analysts to build and deploy models with no coding knowledge for a 12x increase in productionalized projects.
Thanks to Dataiku, Regeneron has a well-established AI and machine learning test bed for quick experimentation. Plus, with the ability for both data and non-data professionals to use and collaborate with Dataiku, Reneneron can bring the right mix of engaged wet-lab scientists, data scientists, and compute specialists with a collaborative spirit.
Dataiku provides Davivienda with a significant advantage by streamlining and simplifying data analysis, manipulation, and modeling tasks, resulting in a more sophisticated and effective solution to address the challenge of portfolio management and collections in our credit institution.
Kaneka uses Dataiku to automate and accurately predict resin drying temperatures in half the amount of time of their previous machine learning solutions. The bottom line? Increased efficiency and reduced costs.
The team of data scientists at Marc O'Polo leverages Dataiku to experiment with sophisticated uplift modeling for print ad optimization. They were ultimately able to impact the business by ensuring they weren't wasting money by sending direct mail to "sure thing" customers.
Kapital Bank built a behavioral scoring model in Dataiku that was 3x faster than previous legacy models to identify small- and medium-sized businesses for which to provide loans.
