Machine learning technology also powers security and network analytics, helping to root out and identify potential risks and intrusions. Enterprises even use the technology for the predictive maintenance of complex machinery, helping to identify potential malfunctions before they become expensive repairs. In fact, machine learning platforms are so common as to belie machine learning's reputation as the highest of high tech. And to be sure, a close look reveals the tech to be highly advanced. Machine learning most accurately describes algorithms that enable applications to make accurate outcome predictions without overt programming or user intervention.

Organizations should first consider the data types and data management features the product or service offers and whether they are available for a customer's on-premises systems, in a private or public cloud, or in a hybrid IT environment. For instance, does the platform support big data initiatives and allow users to build machine learning models with data gathered from a variety of sources, including text, images, multimedia and location systems? Data preparation features should also factor into a buying decision and machine learning platforms comparison, including data aggregation, sorting, filtering and integration. Does the platform handle data preparation faster by identifying common quality problems, for example? Also important are the visualization and exploratory tools the product or service offers.

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With the proliferation of vendors, doing a machine learning platform comparison can be a dizzying process. But most of the features prized by users and experts come back to the platform's flexibility. "We just want to be able to pick the right tool for the right job," said Chris Robison, lead data scientist at Overstock.com. In a webinar hosted by Databricks, the San Francisco-based company offering managed Spark products, Robison described how his team uses Databricks' software to score site visitors on their propensity to purchase. This involves first taking raw web log data and attaching features to it.

Data scientists who want to build machine learning models and put them into production have no shortage of available tools, but choosing the right one comes with some thorny decisions. Note that many open source tools are available for machine learning, as well as other vendor offerings, but we focused exclusively on vendor cloud platforms that span the entire machine learning lifecycle from data ingestion to model development to production. The market for machine learning platforms is heating up, and all of the leading vendors are looking to nab their share. Several vendors have beefed up their offerings in recent months and now offer simple, cloud-based platforms for getting started with machine learning and developing models that can quickly be put into production.

Data scientists who want to build machine learning models and put them into production have no shortage of available tools, but choosing the right one comes with some thorny decisions. Note that many open source tools are available for machine learning, as well as other vendor offerings, but we focused exclusively on vendor cloud platforms that span the entire machine learning lifecycle from data ingestion to model development to production. The market for machine learning platforms is heating up, and all of the leading vendors are looking to nab their share. Analyst firm Forrester expects this market to grow at a rate of 15% annually through 2021. Several vendors have beefed up their offerings in recent months and now offer simple, cloud-based platforms for getting started with machine learning and developing models that can quickly be put into production.