DataRobot 5 Latest Trends in Enterprise Machine Learning BKG 2
White Paper

5 Latest Trends in Enterprise Machine Learning

Organizations are under growing pressure to transform the volumes of data captured by their systems into valuable insights that drive impact across all levels and lines of business.

Investing in AI/ML is no longer optional but critical for organizations to remain competitive. Yet, this growing investment also brings challenges. AI remains complex and out of reach for many. Outcomes that drive real business change can be elusive. And as investments in AI/ML grow, many are left contending with increasingly challenging operational concerns and technical debt. The reality is that the vast majority of organizations still struggle to bring models into production and maximize their business impact.

By deeply exploring the AI/ML strategies of over 400 organizations across industries, DataRobot has gained unique insights into how AI is unlocking economic growth, as well as the common hurdles facing organizations, at a time when this has never been more critical. This report explores the latest enterprise AI/ML trends unfolding in 2021, and how organizations are turning to AI and ML to drive business value and gain a competitive advantage.

Download this report to learn:

  • Our findings from our recent survey of 400+ organizations investing in AI/ML
  • Five key trends in enterprise AI/ML that have emerged throughout 2021
  • Why organizations struggle to get value out of their AI/ML investments, and how you can plan for success in 2022 and beyond

** In July 2021, DataRobot acquired Algorithmia. The survey was conducted by Algorithmia prior to the acquisition. **

  • Organizations need to run their models close to the data and applications that need them most—and increasingly, this means running those models across geographic and regulatory boundaries. This underscores the need for an end-to-end AI platform with MLOps that supports distributed serving.
    Kenny Daniel
    Kenny Daniel

    CTO of MLOps, DataRobot

  • The need to leverage machine learning for better and faster insights is clear. Only organizations that are able to rein in the complexities around infrastructure, tooling, operations, and workloads will be able to deliver on the value of those insights. The explosion of AI/ML technologies across the data science ecosystem simultaneously provides broader choice for users and increases integration overhead between systems that erodes data governance. To tackle this complexity, one strategy is to think in terms of your data being a gravity center, and decide if the various tools, platforms, and analytics capabilities can be organized around it. In doing so, you can determine the essential AI/ML components that orbit around your data and minimize friction between tools.
    Paul Zhao
    Paul Zhao

    Principal Product Manager, Data Science and Machine Learning, Snowflake

  • The COVID pandemic has really brought MLOps front and center for business applications of AI/ML. With the world changing rapidly around models, it became critical to be able to detect changes in near real time and to be able to deploy refreshed models rapidly into production. The biggest challenges to speed in MLOps relate to the ever-growing requirements around security and controls. The most successful products in this space will have extensive security and controls built-in.
    Dave Castillo

    PhD, Firmwide Head of AI/ML Technology, JPMorgan Chase