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Democratizing Machine Learning: The More, The Merrier!

by on April 10, 2015

The machine learning marketplace is heating up. The latest news in the machine learning front was Amazon’s launch of Amazon Machine Learning, which follows a few months on the heels of the commercial release of Azure Machine Learning from Microsoft.  These forays from technology stalwarts (along with IBM Watson) show that the marketplace is ready for machine learning at scale, which certainly reflects the growing business imperative to be able to make smarter decisions from Big Data backends. And more companies providing machine learning solutions is good for the industry at large:  it provides customers with more choices, and will further hasten the pace of innovation from machine learning providers, including BigML.

While BigML clearly isn’t as big as Microsoft, Amazon and the like we do have the benefit of perspective as we were the first company to bet on democratizing machine learning way back in 2011. (At that time Google Prediction API existed but was only oriented to developers, and hasn’t evolved much since).  Rather than pointing out that imitation is the sincerest form of flattery (and yes, we are flattered!), we think this is a good opportunity to highlight some top attributes of BigML in relation to emerging solutions on the marketplace.

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BigML provides a robust, full-featured and scalable platform which has been informed by feedback from over 17,000 users who have created tens of millions of predictive models and machine learning tasks that have supported a countless number of predictions.

  1. Key differentiators of the BigML platform include:
  • Support for both supervised and unsupervised learning techniques:  in addition to classification and regression tasks solved by interpretable decision trees or ensembles for top tier performance,  BigML supports cluster analysis and anomaly detection.  And our 2015 roadmap is chock full of added algorithms and techniques for data exploration.
  • Best-of-market interface and visualizations: “Beautiful” “wow” and “amazing” are typical reactions I’ve heard while presenting BigML to customers and conferences.  Check it out for yourself and let us know of another interface that is as rich, enjoyable and intuitive as BigML.
  • Full-featured REST API for programmatic access to advanced ML capabilities, with bindings in several languages:  as beautiful as our interface may be, the brawn and brains of BigML rests in our open API that developers and analysts alike can use to quickly create predictive workflows and other machine learning tasks.
  • Easy sharing of resources and models, including the ability to export models from BigML locally and/or for incorporation into related systems & services:  want to export a model from Azure or Amazon ML?  Good luck with that.  BigML makes it easy to export your models via the interface or API, and you’re free to use your models wherever you wish.
  • BigML Private Deployments can be implemented in any cloud and/or on premise:  As BigML penetrates deeper into the enterprise, our willingness and ability to run in a corporate datacenter has become a critical differentiator.  In addition, we’ve implemented BigML not just on AWS, but also in the Azure and other public and private clouds.
  • In-platform feature engineering and data transformations:  BigML’s Flatline makes it easy to extend and create new features for you dataset, without having to go back to your source – both in the BigML interface and programatically using a rich set of predefined, ML-aware functions or building your own.
  1. BigML is suitable for developers and enterprises alike:
  • Pricing starts at $30/mo for individual users & developers – and you can actually use BigML for free in our Developer mode for tasks under 16MB.
  • Enterprises can purchase fully loaded “custom” subscriptions (bundled with training, support and more) and/or implement a BigML Private Deployment – either in the cloud or behind their firewall
  • All of these approaches (subscriptions or Private Deployments) include unlimited machine learning tasks along with the ability to export models.
  • BigML never charges subscribers for predictions against your own models (in contrast to Azure and Amazon)
  • With BigML subscriptions you can train models as many times as you want — and in parallel — at no extra fee
  1. BigML offers customers both an advanced analytics platform as well as a foundation for development and deployment of predictive applications:
  • It was almost two years ago when Mike Gualtieri at Forrester stated “predictive apps are the next big thing” – and we here at BigML are seeing the reality of that vision on a daily basis both with ISVs and with enterprise developers.
  • As BigML models can be exported, they can easily be incorporated into apps and services – enabling developers to focus on their solution rather than in creating and maintaining ML algorithms
  • BigML offers expert services (directly and through our partners) to help with development and deployment of predictive apps

Beyond the tangible differences listed above, as a nimble, hungry company BigML will constantly innovate at a furious pace to meet and exceed our customers’ needs.  We’re passionate about supporting our users and engage with our enterprise customers on a very integrated basis to ensure not only the success of their implementations, but also that our platform evolves according to current and emerging business requirements.

Want to learn more about BigML and/or get an update on our latest & greatest features?  Contact us and we’ll be happy to run you through a demonstration and discuss our various engagement options.  Or, you can simply get started today!

One Comment
  1. Reblogged this on Oleg Baskov.

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