Operationalizing AutoML: ML Ops

Predictions are interesting on their own. They are valuable when put into production.

Operationalizing AutoML – often called “ML Ops” – means putting AutoML predictions into regular workflows to change business outcomes. Here are a few ways do it.

Graphical Interface
Saas AutoML tools have a GUI that enables training data ingestion, data prep, feature engineering, and model building. Once an optimal model is made, predictions are created on production data. Simply exporting the predictions from the GUI delivers a data file for people or other systems to act upon. For example, predictions the prioritize sales lead follow-up could be handed to sales ops as a daily call list, or could be imported to a marketing automation system as an email segmentation list.

API
Application Programming Interfaces (APIs) are specifications that describe how dissimilar systems can communicate reliably. AutoML APIs can be hooked to enterprise systems to automate export of predictions to eliminate manual file handling.

Model Export
AutoML produces executable code, typically Java bytecode, that can be run wholly outside the AutoML tool itself. Models so exported can be run again and again on production data as often as required. When models are improved based on new training data, ML ops can simply replace the executable code with the new models.

Custom Deployment
Some applications, such as real-time predictions, require close integration with cooperating systems. Customized data pipelines can be created to manage these processes.

The take-away: You can begin using AutoML results to improve business performance right now. As needs expand, there are many options for blending ML Ops into automated workflows, and Squark can help with all of them.