Continuous Delivery for Machine Learning Applications with Open Source Tools

Accelerate ML innovation with continuous delivery (CD) using open-source tools. Learn how CD streamlines ML development, improves model quality, and reduces risk.

Key takeaways
  • Continuous Delivery (CD) for Machine Learning (ML) applications is a mindset shift that brings people and roles together to deliver software faster and more reliably.

  • CD for ML involves versioning data, models, and code, as well as automating the deployment process.

  • A cross-functional team is essential for successful CD for ML, as it requires collaboration between data scientists, data engineers, developers, business analysts, and operations teams.

  • Observability is key to monitoring the performance of ML models in production and ensuring that they are meeting business objectives.

  • Automating the deployment process can help to reduce the time it takes to get new models into production, which can lead to faster innovation and improved business outcomes.

  • CD for ML can help to improve the quality of ML models by ensuring that they are tested and validated before they are deployed to production.

  • CD for ML can also help to reduce the risk of model failure by providing a way to quickly roll back to a previous version of a model if necessary.

  • **Overall, CD for ML can help organizations to deliver ML applications faster, more reliably, and with higher quality.