Dataiku, a leading provider of Artificial Intelligence (AI) platform, recently made enhancements to its toolset to scale AI. The new release, Dataiku 11, will enable organizations to deliver valuable solutions and allow them to seamlessly engage with AI to drive business objectives.
Dataiku 11 will provide enhanced AI governance, speed workflows, and help organizations in preventing customer churn. It will enable automatic flow documentation and proactive model stress testing to elevate AI models and help in building trust with data consumers and stakeholders by offering central visibility into all types of data and analytics projects.
Some of the key features of Dataiku 11 include:
• Built-in tools to reduce technical overhead and increase efficiency while crafting custom codes, sourcing high-quality datasets, and performing model experiments.
• A collaborative and managed framework for annotating images. It will ensure proper alignment between labelers, modelers, and experts.
• End-to-end visual path for computer vision task in order to help users to handle complex object detection and image classification use cases.
Clément Stenac, CTO and Co-founder of Dataiku, said, “Expert data scientists, data engineers, and ML engineers are some of the most valuable and sought-after jobs today. Yet all too often, talented data scientists spend most of their time on low-value logistics like setting up and maintaining environments, preparing data, and putting projects into production. With extensive automation built into Dataiku 11, we’re helping companies eliminate the frustrating busywork so companies can make more of their AI investment quickly and ultimately create a culture of AI to transform industries.”
In addition to making the lives of tech experts easier, Dataiku 11 will also help non-coders to fully realize the power of AI tools. The new tools consist of:
• Visual time series forecasting that will allow experts to create business forecasting models without coding.
• Centralized feature store and sharing workflows to help teams to reuse and speed up projects responsibly.
• What-if accelerators to allow teams to evaluate and understand how to optimize business outcomes.
