On Demand Webinar: Why more than half of ML models don’t make it to production

Download the Presentation On-demand

As the commercial world accelerates investment into AI and Machine Learning one theme continually appears. Models are being built, but they are not being used. Teams of Data Scientists around the world are training versatile models but due to managerial, logistical and infrastructural problems, these models are not making it to production.

In this webinar, Solutions Architect Aaron Schneider will diagnose the problem and identify the symptoms. He’ll explain how reproducibility, scalability and collaboration can increase the gap between research and production. The webinar will examine best practices for building a machine learning pipeline that enables quick iteration, deployment and CI/CD to ensure that your company is deploying and maintaining the best services for you customers and clients.


Key takeaways:

  • The common issues that block deployment and increase time to production
  • How different stakeholders can resolve key issues
  • How to accelerate from research to production
  • Tools that can make productionizing models easy
  • Leveraging Kubernetes and container-based architecture for faster deployment

As the commercial world accelerates investment into AI and Machine Learning one theme continually appears. Models are being built, but they are not being used. Teams of Data Scientists around the world are training versatile models but due to managerial, logistical and infrastructural problems, these models are not making it to production.

In this webinar, Solutions Architect Aaron Schneider will diagnose the problem and identify the symptoms. He’ll explain how reproducibility, scalability and collaboration can increase the gap between research and production. The webinar will examine best practices for building a machine learning pipeline that enables quick iteration, deployment and CI/CD to ensure that your company is deploying and maintaining the best services for you customers and clients.


Key takeaways:

  • The common issues that block deployment and increase time to production
  • How different stakeholders can resolve key issues
  • How to accelerate from research to production
  • Tools that can make productionizing models easy
  • Leveraging Kubernetes and container-based architecture for faster deployment
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