Enterprises have struggled to collaborate well around their data, which hinders their ability to adopt transformative applications like AI. The evolution of DataOps could fix that problem. The term DataOps emerged seven years ago to refer to best practices for getting proper analytics, and research firm Gartner calls it a major trend encompassing several steps in the data lifecycle. Just as the DevOps trend led to a better process for collaboration between developers and operations teams, DataOps refers to closer collaboration between various teams handling data and operations teams deploying data into applications. Getting DataOps right is a significant challenge because of the multiple stakeholders and processes involved in the data lifecycle.
Dan Wright just became CEO of DataRobot, a company valued at more than $2.7 billion that is promising to automate the building, deployment, and management of AI models in a way that makes AI accessible to every organization. Following the release of version 7.0 of the DataRobot platform, Wright told VentureBeat that the industry requires a new era of democratization of AI that eliminates dependencies on data science teams. He explained that manual machine learning operations (MLOps) processes are simply not able to keep pace with changing business conditions. This interview has been edited for brevity and clarity. VentureBeat: Now that you're the CEO, what is the primary mission?
MLOps, a compound of "machine learning" and "information technology operations," is a newer discipline involving collaboration between data scientists and IT professionals with the aim of productizing machine learning algorithms. The market for such solutions could grow from a nascent $350 million to $4 billion by 2025, according to Cognilytica. But certain nuances can make implementing MLOps a challenge. A survey by NewVantage Partners found that only 15% of leading enterprises have deployed AI capabilities into production at any scale. Still, the business value of MLOps can't be ignored.
The advancements in machine learning has more and more enterprises turning towards the insights provided by it. Data scientists are busy creating and fine-tuning machine learning models for tasks ranging from recommending music to detecting fraud. Here's what a machine learning model lifecycle looks like: According to Wikipedia, "MLOps ('Machine Learning' 'Operations') is a practice for collaboration and communication between data scientists and operations professionals to help manage the production ML lifecycle. MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. MLOps applies to the entire lifecycle - from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics." So, is MLOps just another fancy name for DevOps?
In this era of industrialization for Artificial Intelligence (AI), enterprises are scrambling to embed AI across a plethora of use cases in hopes of achieving higher productivity and enhanced experiences. However, as AI permeates through different functions of an enterprise, managing the entire charter gets tough. Working with multiple Machine Learning (ML) models in both pilot and production can lead to chaos, stretched timelines to market, and stale models. As a result, we see enterprises hamstrung to successfully scale AI enterprise-wide. To overcome the challenges enterprises face in their ML journeys and ensure successful industrialization of AI, enterprises need to shift from the current method of model management to a faster and more agile format.