Dataiku
Resources
Fraud and Anomaly Detection
A broader overview of the role of anomaly detection in banking (beyond fraud) and ways to integrate the process into existing workflows.
How to Improve Data Quality
This ebook discusses the importance of data quality in any end-to-end AI project, with a specific focus on the need for data labeling through active learning.
Foundations for Architecting Data Solutions
Authors Ted Malaska and Jonathan Seidman guide you through the major components necessary to start, architect, and develop successful big data projects.
Why Enterprises Need Data Science, Machine Learning, and AI Platforms
Delivering results with Artificial Intelligence
The AI Impact Survey: Insights from More than 400 Data Professionals
Thoughts from 400+ Data Professionals Worldwide More than 400 data professionals in San Francisco, Amsterdam, Paris, and Stuttgart responded to the AI Impact Survey. This ebook breaks down the results and includes recommendations as well as best practices for organizations looking to scale AI.
AI: From Cost to Revenue
For an Enterprise AI strategy to be truly sustainable, one must consider the economics: not just gains, but cost. This ebook unpacks those costs and how to control them.
Why Enterprises Need Data Science, Machine Learning, and AI Platforms
Enabling Enterprise AI Enterprise AI is at peak hype, yet AI has yet to fundamentally change most businesses. For the most part, those that have been successful have democratized the use of data throughout the organization. But how can everyone else catch up? Enter: data science, machine learning, and AI platforms.
Defining A Successful AI Project
How should organizations choose their AI projects? Should it be a trendy, consumer-facing solution such as a chatbot, virtual assistant, or computer vision application? Or is it better to focus on embedded, back-of-the-house projects that quietly help to improve internal operations, employee efficiency, or decision-making? This ebook has answers.
What Is MLOps?
With this report, business leaders will learn about MLOps, a process for generating long-term value while reducing the risk associated with data science, ML, and AI projects. Authors Lynn Heidmann and Mark Treveil from Dataiku start by introducing the data science-ML-AI project lifecycle to help you understand what--and who--drives these projects.
Getting the Most Out of AI in 2021: Insights From 10+ Industry Trailblazers
To help organizations continue to deftly pivot and keep pace in an ever-evolving world, we compiled qualitative commentary from a diverse range of technical and non-technical experts from companies like Snowflake, Microsoft, AWS, and Tableau, among others.