Once the data science is done (and you know where your data comes from, what it looks like, and what it can predict) comes the next big step: you now have to put your model into production and make it useful for the rest of the business. This is the start of the model operations life cycle. The key focus areas (detailed in the diagram below) are usually managed by machine learning engineers after the data scientists have done their work.
Disclaimer: the scenario below is hypothetical. Any similarity to any specific telecommunications company is purely coincidental.
Although we use the example of a telecommunications company the following applies to every organization with customers or voluntary stakeholders.
Imagine that you are a Chief Data Officer at a major telecommunications provider and the CEO has asked you to overhaul the existing customer churn analytics. The current process relies on manual export of data from dozens of data sources including ERP,
Spark ML is one of the dominant frameworks for many major machine learning algorithms, such as the Alternating Least Squares (ALS) algorithm for recommendation systems, the Principal Component Analysis algorithm, and the Random Forest algorithm.
[Editor’s note: This article was originally published on the Hortonworks Community Connection, but reproduced here because CDSW is now available on both Cloudera and Hortonworks platforms.]
Using Deployed Models as a Function as a Service
Using Cloudera Data Science Workbench with Apache NiFi, we can easily call functions within our deployed models from Apache NiFi as part of flows. I am working against CDSW on HDP (https://www.cloudera.com/documentation/data-science-workbench/latest/topics/cdsw_hdp.html),
We are excited to announce the general availability of Cloudera Altus SDK for Java to programmatically leverage the Altus platform-as-a service for ETL, batch machine learning, and cloud bursting. Altus empowers customers and partners alike, to run data engineering workloads in the cloud, leveraging cloud infrastructures such as AWS. Cloudera Altus also provides the ability to create data engineering pipelines using both a web console and CLI.
Cloudera Altus SDK for Java was developed to provide easier programmatic access with the popular Java programming language so that users can automate their data engineering workloads.