Continuous delivery for machine learning

 

Getting machine learning applications into production is hard

In modern software development, we’ve grown to expect that new software features and enhancements will simply appear incrementally, on any given day. This applies to consumer applications such as mobile, web, desktop apps as well as modern enterprise software. We’re no longer tolerant of big, disruptive, deployments of software. Thoughtworks has been a pioneer in Continuous Delivery (CD), a set of principles and practices that improve the throughput of delivering software to production, in a safe and reliable way.

As organizations move to become more “data-driven” or “AI-driven”, it’s increasingly important to incorporate data science and data engineering approaches into the software development process to avoid silos that hinder efficient collaboration and alignment. However, this integration also brings new challenges when compared to traditional software development.

 Read more: https://www.thoughtworks.com/insights/articles/intelligent-enterprise-series-cd4ml

Overview of Different Approaches to Deploying Machine Learning Models in Production

 

Overview of Different Approaches to Deploying Machine Learning Models in Production

Learn the different methods for putting machine learning models into production, and to determine which method is best for which use case. 

 

There are different approaches to putting models into productions, with benefits that can vary dependent on the specific use case. Take for example the use case of churn prediction, there is value in having a static value already that can easily be looked up when someone call a customer service, but there is some extra value that could be gained if for specific events, the model could be re-run with the newly acquired information.

There is generally different ways to both train and server models into production:

• Train: one off, batch and real-time/online training
• Serve: Batch, Realtime (Database Trigger, Pub/Sub, web-service, inApp)

Each approach having its own set of benefits and tradeoffs that need to be considered.

Read more: https://www.kdnuggets.com/2019/06/approaches-deploying-machine-learning-production.html