Machine Learning Operations (MLOps)

Delivering the capabilities that Data Science and IT Ops teams need to work together to deploy, monitor, manage, and govern machine learning models in production.

The share of AI models created but never put into production at large enterprises has been estimated to be as high as 90% or greater. With massive investments in data science teams, platforms, and infrastructure, the number of undeployed AI projects is dramatically increasing — along with the number of missed opportunities. Unfortunately, most projects are not showing the value that business leaders expect and are introducing new risks.

AI 성공의 장애물

  • Difficulty with deployment: Data science teams are using a variety of ML platforms, languages, and frameworks that rarely produce production-ready models. Wary IT teams are unwilling or unable to deploy code they don’t understand.

  • Flying blind: With predictive models, prediction accuracy can decrease without changes in traditional indicators like memory usage or response time, requiring new monitoring methods and metrics.

  • Complex updates: AI-based applications have a complex lifecycle, including frequent updates that, when done manually, are time-consuming. Model updates also require significant production testing and validation to maintain production model quality.

  • New risks and regulations: IT operations machine learning applications need strong governance practices and tools to minimize risk and ensure regulatory compliance, which many organizations have not put into place.

mlops solution


DataRobot MLOps delivers the capabilities Data Science and IT Ops teams need to work together to deploy, monitor, manage, and govern machine learning models in production. With DataRobot MLOps, companies can:
  • Easily deploy machine learning projects written in modern languages and frameworks, on modern production infrastructures such as Kubernetes on any cloud or on-premise system.
  • Monitor ML-based applications for performance issues with ML-centric capabilities like data drift analysis, model-specific metrics, and alerts. Provides proactive management and timely updates that don’t waste resources and ensure continued application performance.
  • Manage the dynamic nature of machine learning applications with the ability to frequently update models, including testing and validation of new models. Update models on-the-fly while continuing to serve business applications.
  • Enforce governance policies related to machine learning models and capture the data that is required for strong governance practices in machine learning operations management, including who is publishing models, why changes are being made, and which models have been deployed over time.

DataRobot MLOps 및 거버넌스와 함께 AI의 실질적인 가치와 ROI 실현:

  • 수개월이 아니라 단 몇 시간만에 모델 배포
  • Proactive ML health monitoring
  • Efficient and trusted model updates
  • ML 거버넌스 모범 사례 내장
더 알아보기

DataRobot MLOps를 통해 비즈니스 리더가 할 수 있는 일

프로덕션 환경에서 머신러닝 애플리케이션의 배포, 지속적인 최적화, 거버넌스를 자동화하여 AI 가치를 실현하는 가장 빠르고 안전한 길을 모색할 수 있습니다.
AI 특화된 프랙티스 및 도구를 통해 전사적으로 머신러닝 도입을 확장할 수 있고, 따라서 현재에는 결과를 제공하고 미래에는 역량을 확보할 수 있습니다.
ML 기반 애플리케이션의 가치 창출을 위해 데이터 사이언스 팀과 IT/운영팀이 같이 일할 수 있도록 협업을 이끌어 냅니다.
프로덕션 환경의 머신러닝 프로젝트를 위한 강력한 거버넌스 도구와 프랙티스를 적용하여 조직 내 리스크를 줄일 수 있습니다.

DataRobot의 라이브 데모를 보고 싶거나, 귀사의 프로젝트에 적합한지 논의하고 싶다면 연락 주십시오.