The short answer is: _you don&#x27;t_. That&#x27;s not how machine learning works at scale.<p>To quote from a great recent article: (I can&#x27;t find the link, sorry)<p>It&#x27;s one thing to create an excellent fraud detection model in R, and quite another to build:<p><pre><code>  - Fault-tolerant ingest of live data at scale that could represent fraudulent actions
  - Real-time computation of features based on the data stream
  - Serialization, versioning and management of a fraud detection model
  - Real-time prediction of fraud based on computed features at scale
  - Learning over all historical data
  - Incremental update of the production model in near-real-time
  - Monitoring, testing, productionization of all of the above

</code></pre>
You don&#x27;t build a data team out of a single person and tack on an easy model to build a company - it takes a team to build a real data project, and those teams are hard to find, hard to recruit, and hard to make successful.