mlops hero2

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.

Barreiras do sucesso da IA

  • 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

Solução

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.

Obtenha valor tangível e ROI da IA com DataRobot MLOps e governança:

  • Implantação do modelo em horas, não em meses
  • Proactive ML health monitoring
  • Efficient and trusted model updates
  • Melhores práticas de governança de ML incorporadas
Saiba mais

Com o DataRobot MLOps, os líderes empresariais podem:

Obter o caminho mais rápido e seguro para o valor da IA automatizando a implantação, otimização contínua e governança de aplicativos de machine learning em produção.
Escale a adoção de machine learning em toda a empresa com práticas e ferramentas específicas de IA para que você possa entregar resultados hoje e ter capacidade para amanhã.
Facilite a colaboração de suas equipes de Ciência de Dados e TI / Ops para trabalharem juntas para criar valor a partir de aplicativos orientados por ML.
Diminua o risco para a organização, implementando ferramentas e práticas de governança fortes para projetos de machine learning em produção.

Se você deseja uma demonstração ao vivo de como o DataRobot funciona ou gostaria de discutir a adequação ao seu projeto, envie um email

info@datarobot.com