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  • The Ultimate Guide to Marketing AI

    To compete and stay relevant in a rapidly changing world, businesses need to embrace the idea of using data and AI for their marketing activities. Download this guidebook for an end-to-end framework for getting started on your path to marketing AI.

  • 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.

  • Enterprise AI for Business Preparedness

    Inside this white paper, learn about AI use cases that businesses can start right now in order to help in a time of economic change and more.

  • Making Data-Driven Investment Decisions

    While investing and asset management are data-driven fields at their core, many smaller buy-side and asset management firms have been slow to embrace data science, analytics, and digitization. Read on to learn how, despite this fact, data science investments by asset management firms are both necessary and profitable.

  • Staffing the AI Enterprise: A Guide to Data Democratization

    Download this Guide to Data Democratization for tips on hiring, upskilling and retaining data talent for your AI-powered enterprise.

  • The Economics of AI: How to Shift Data Projects from Cost to Revenue Center

    In the coming years, the ability of organizations to pivot their activities around Enterprise AI will fundamentally determine their fate. Download this white paper to learn about the economics of implementing and running an AI program.

  • How to Improve Data Quality with Data Labeling

    Data needs to be valuable to drive machine learning model success. In this white paper, Dataiku will discuss 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. Read on to discover the benefits of active learning, challenges associated with it, and use cases.

  • Artificial Intelligence in Insurance: High-Value Use Cases

    The growth of data science is driven by a variety of factors, including risk modeling. This white paper explores up-and-coming use cases for data science and machine learning in the insurance space, challenges traditional insurance companies face in implementing these use cases, and trends in how successful companies execute. Download a copy today.

  • The AI Impact Survey Insights from More than 400 Data Professionals

    Many data professionals have strong opinions about the ways in which AI will impact their industries, companies, and roles—but not all these opinions align with reality. Read this white paper to learn how data professionals expect AI to change their industry, as well as how to integrate these predictions into your business strategy.

  • A Framework for Choosing the Right Use Cases

    As hype around data science, machine learning, and AI continues to grow, more and more organizations are feeling the pressure to modernize their business.

  • The State of the Market

    In order to go in-depth on what exactly data science and machinelearning (ML) tools or platforms are, it's important to take a step back and understand where we are in the larger story of AI, ML, and data science in the context of businesses.

  • Trends in Enterprise Data Architecture and Model Deployment

    To stay on the edge of business innovation, companies are investing in cloud and AI technology, among other innovations. But how prevalent are these trends? And do they produce real results? Read this analyst report what data architecture and model deployment trends are happening right now, and where companies plan to focus their data efforts next.

  • The Data ROI Toolkit: How to Determine the Value of Your Data Initiatives

    This white paper by Dataiku gauges your company’s ultimate ROI—balancing pros like time saved and reduced data expenditure against cons like workflow disruption and the cost of additional training. Download it to see ways that your company can make sure to improve your ROI and derive true value from your data.

  • 2020: What's Next for the Data-Driven Enterprise

    Read this analyst report to learn the 6 AI trends to watch for in 2020 and beyond to make sure your organization is not left behind, as well as key factors that will evolve the role of the data scientist.

  • Defining a Successful AI Project: how can organizations prioritize the right projects?

    Leveraging a centralized data platform might be the secret to staying ahead of the technological curve. Access this white paper to read the 5 Ws and an H AI prioritization check list, learn how to avoid false starts on AI projects that are ill-defined, and achieve an environment of success.

  • How to scale up AI competencies within organizations in the energy and utility industries.

    Resource and utility companies that hope to remain competitive should be aggressively pursuing the next technological frontier. Access this white paper to learn the 3 immediate steps that companies looking to advance in the race to AI can take as well as some of the highest-value AI use cases to date.

  • Innovative data strategies for smarter predictions

    By leveraging a collaborative and flexible AI-based tool, Europcar was able to build a predictive web app as well as dashboards that forecasted market activity at a very granular level. Access this case study to learn more about the technology Europcar used and the positive impact the solution had.

  • Machine Learning Applications for Banking Fraud Detection

    AI-based systems can augment regulatory alert systems and improve analysts’ workflow by reducing noise without discarding results. Access this paper to view the 3 types of anomalies to look out for, understand examples of how banks use fraud and anomaly detection, and learn how to build a basic, machine learning-based fraud detection system.

  • Fraud Detection in Healthcare: a step-by-step guide to incorporating machine learning

    There are many AI-based use cases that span the healthcare industry, all with the goal of improving patient care. Access this white paper to learn the 3 basic types of anomalies that may be detected and to learn how to confront the challenges of fraud and anomaly detection with 4 AI and ML-based approaches.

  • Data Architecture Basics: an Illustrated Guide for Non-Technical Readers

    Don't assume that only CIOs or data architects should understand data structure, it's a skill that every member of an organization should be familiar with if true data democratization is the goal. Read this white paper for data architecture basics that can integrate any team member into the mission for data-driven decision-making.

  • Data Science Operationalization

    Read this white paper to find common ground between data science and IT teams for the benefit of your data and analytics projects as a whole by ensuring consistent packaging and deployment of models.

  • How and Why IoT is Shifting Enterprise AI Strategy

    Businesses will not be able to yield the full benefits of IoT integration without a better structure for their data and analytics—and this means mastering AI and ML too. Read this white paper to learn how to enable true innovation through data and IoT.

  • Self-Service Analytics at GE Aviation: So Much More than BI

    In the case of GE Aviation, they were able to boast innovative new products and huge efficiency boosts by implementing a self-service analytics solution—ensuring that all business efforts could be quantified at scale. Read this case study to learn how.

  • Data Science, Machine Learning, AI Platforms: How & Why?

    Ultimately, it's about saving time and getting organizations into the AI game sooner rather than later—read this white paper to learn more about data science and ML platforms and how they allow for more productive data initiatives.

  • How to reach data maturity and become a data driven organization

    CDOs can drive data strategy, support machine learning analysis, and generate business value with data. Read this white paper (including insights from over 50 CDOs worldwide) to learn how to create an organizational path to data leader success.

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