ML Tools

When working on a machine learning problem, one follows the following high level steps.

  1. Gathering and exploring data
  2. Prototype and build models; evaluate and select best model
  3. Deploy model so that it’s accessible as a web service
  4. Visualization to consume output of models

The below table shows a quick grouping of the tools available for each of the phases of machine learning algorithm development.

[mk_table title=”ML Tools” style=”style1″]

Model Prototyping and Building R or Python programs on Desktop or on  AmazonR code on software from companies like Yhat, Domino Data Labs, AzureML, Amazon ML, BigML
Deploying models as web services Open source rApache, hosted on AmazonSoftware from Revolution Analytics or Yhat, AzureML
Big Data (data that’s bigger than what can fit on a single machine) Apache, Spark,, Hadoop, Google Cloud Dataflow
Data Visualization and Consumption Salesforce Wave, Tableau, Domo