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  • AI & Data Analytics in Financial Services: A Use Case Library

    In this 41-page e-book, you’ll discover a library of use cases for analytics and AI designed to help financial services organizations get ahead in their data journey. Read on to learn how you can develop a competitive advantage across all the different functions of your organization.

  • Tackle the Right AI Projects for the Best ROI

    Avoiding data or AI project failure starts with ensuring the fundamentals are in place before any given project is even off the ground. One of those key fundamentals is actually choosing the right project. Read on to understand how you can prevent data and AI problems before they happen and foster an environment for success.

  • IT Leaders: Winning the Analytics & AI Race

    Despite the increasing adoption of AI, there are still many obstacles that IT organizations must overcome. So how can organizations accelerate data product delivery without losing control? In this e-book, you’ll learn about different strategies and approaches IT leaders can utilize to overcome data obstacles and scale AI. Read on to learn more.

  • Beyond the Chatbot: The Path to Enterprise-Grade Generative AI

    Generative AI and large language models (LLMs), such as ChatGPT, Bard, and Bing, are fundamentally transforming the business landscape. This e-book aims to help you answer that question, by giving you the information you need to understand and prepare for the future of LLMs. Read on to learn more.

  • Balance AI Quick Wins and Long-Term AI Transformation

    Reaching Everyday AI, the ability to build AI into an organization’s daily operations and processes, is the ideal for many organizations beginning their AI transformation journeys. In this e-book, Dataiku guides you through the AI strategy formulation process, sharing insights to help you get the most out of your AI endeavor. Read on to learn more.

  • Build Responsible Generative AI Applications: Introducing the RAFT Framework

    Yes, Generative AI (GenAI) presents myriad opportunities. But GenAI can also pose myriad risks, especially if an AI system is not responsibly designed, deployed and governed. In this 16-page e-book, discover what constitutes responsible GenAI and learn how to implement a responsible approach at your own organization.

  • Advancing Analytics in Finance Teams

    How can you improve the efficiency of regular financial processes to free up time for proactive, value-add work? The answer is with a centralized analytics and AI platform that enables you to build reporting, pipelines, and models. Read on to learn how you can equip your finance teams with the ability to answer more questions with data, faster.

  • Using ChatGPT, GPT-4, & Large Language Models in the Enterprise

    There is no one-size-fits-all approach to AI for the modern enterprise. And as new AI solutions rise in popularity, it is crucial to understand that many of these products are built on top of a class of technologies called Large Language Models (LLMs). Read on to learn how you can determine the AI strategy that is best suited for your organization.

  • Ebook Upskilling: How to Win the Battle for Data + AI Talent

    Demand for data science managers is high and prices are rising. That’s why McKinsey says that you can no longer hire and outsource your way out of the tech talent problem. Read on to learn about best practices for upskilling your data analytics workforce to unleash the power of AI, including 3 initiatives designed to help drive adoption and ROI.

  • The Ultimate Playbook for Scaling AI

    This 47-page e-book presents the part different people or roles must play in organizational transformation, providing tips, keys to success and real-life stories about scaling AI efforts for each. Read on to learn how your organization can become more efficient in your efforts to scale AI initiatives across the enterprise.

  • Building Self-Service Analytics in the Age of AI

    Read this e-book for a helpful guide to self-service analytics and to learn about supporting reasons to believe that self-service analytics isn’t going anywhere, shortcomings to look out for and how the concept of Everyday AI fills those gaps to make self-service more scalable and more valuable.

  • Machine Learning for High-Risk Applications

    Today, ML systems make high-stakes decisions in high-risk applications throughout the world, but algorithmic discrimination and data privacy violations both present major risks. If you'd like to sever the potential for your ML technologies to be abused, check out this e-book for the best practices behind automated decision-making design.

  • How to Move Beyond ML Predictions: An Introduction to Causal Inference

    Causal inference (CI), The scientific discipline that deals with the relationship of cause and effect within data, is important in that it succeeds in making predictions based off causation and not simply correlation. Access the full PDF and see how CI surpasses ML in its ability to account for the what-ifs of the future.

  • Getting Data Quality Right: A Guide for CDOs and Data Executives

    Data quality and accuracy is an objective not only for businesses leading the way in machine learning and AI, but for anyone who deals with customer information or any kind of data. Studies show that poor data quality can cost companies upwards of 20% of their revenue according to Gartner. Read more about data quality and how you can achieve it.

  • How To: Monitor and Detect Disaster Grant Fraud

    It is an unfortunate fact, when disaster relief programs are established, fraud and abuse follow. Agencies that administer the release of emergency funds need security. This eBook is a step-by-step guide designed to help bolster fraud detection capabilities by using machine learning-based fraud detection. Read on to learn more about this solution.

  • Introducing MLOps

    Traditional machine learning operations were fairly simple and easy to manage; but as ML grows in complexity and scope, the old way of doing things is no longer feasible. ML projects are often started in response to C-suite goals and involve employees across the length of an organization. Check out this eBook to learn more about MLOps.

  • A Framework for Choosing the Right Use Cases

    How do you know if you AI project is a success? Learn how to define and measure AI success.

  • The State of the Market

    Organizations looking to incoroproate machine learning and AI into their large-scale analytics need a certain kind of infrastructure. Learn what enterprise AI platforms bring to the table and how to evaluate them.

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