We’re thrilled to announce that in August we reached a new milestone of 50,000 registered customers on our multi-tenant platform thanks to the accelerating demand for practical Machine Learning worldwide. Back in 2011, we set out with the singular focus of making Machine Leaning beautifully simple for everyone. In sharp contrast with when we launched the first version of our Machine Learning offering, creating an ML-literate professional class has finally become a business imperative across many industries. The growth prospects for BigML look great, so we fully expect to onboard the next 50,000 a magnitude faster since the platform is more complete, the learning tools are in place and the market demand is at an all time high.
Best-in-Class Machine Learning Algorithms
The first version of BigML only featured decision trees as part of a very simple workflow that supported file imports and the ability to make form-based single predictions. Over time, BigML has evolved to not only support more algorithms but also multiple options for automation of workflows all the while abstracting infrastructure layer concerns from the analytical end-user in a scalable manner. BigML’s current version supports highly optimized ensembles (bagging, random decision forest, boosted trees), logistic regression, cluster analysis, anomaly detection, association discovery, topic modeling, and the latest addition, time series forecasting. All of these were implemented from scratch in Clojure (including the Magnum Opus algorithms for association discovery) as opposed to gluing disparate open source libraries to avoid a fragmented and broken user experience.
Leading the Machine Learning Platform Market
Building a platform has proven very difficult and time-consuming even for those with access to talent and deep pockets. We’ve seen many technology companies attempt at cracking the commercial Machine Learning tools opportunity over the years. However, the ambitious press releases and polished on-stage claims and presentations at developer conferences were seldom followed by complete, widely adopted, easy-to-use products that really gained traction in the market.
The smaller, distributed yet highly devoted BigML team’s unadulterated, “hype-free” best practices approach to Machine Learning is especially attractive to companies that prioritize the cost effective delivery of real-life custom Machine Learning solutions above all else. These trailblazing businesses possess proprietary data sources that are transformed into insights that improve operational efficiencies or enable brand new products or services.
Notably, this is achieved by training their own knowledge workers on BigML instead of relying on expensive “hired guns” that may build working systems for isolated use cases, but fail to deliver the longer term transformational impact that Machine Learning promises. Despite little or no Machine Learning experience, BigML trainees reach a level of proficiency that allows them to make Machine Learning part of their everyday problem-solving skills. Over time, this approach introduces a compounding effect through many different predictive use cases that were initially overlooked.
In fact, this self-sufficient path built on top of a standardized Machine Learning framework is nothing new. Some of the leading corporations such as Facebook, Uber and AT&T have already heavily invested in their own platforms (FbLearner Flow, Michelangelo, AT&T Machine Learning System respectively) and achieved widespread Machine Learning usage that goes way beyond small teams of scientists or researchers. However, tens of thousands of other businesses can’t afford to follow the same strategy, which makes it an imperative to evaluate alternatives such as BigML to get a head start.
Given the mandate to open Machine Learning to many more employees, BigML provides an ideal platform to initiate them by teaching the fundamentals of Machine Learning. BigML’s distributed framework is unique in offering Serverless Machine Learning that is accessible in the cloud, in a Virtual Private Cloud, or on-premises. Machine Learning is made easy through BigML’s:
- Dashboard: intuitive web UI with interactive, easy-to-understand visualizations
- API: full programmatic access for developers complete with bindings for popular languages such as Python as well as BigMLer, our command-line interface for the platform.
- WhizzML: our Domain Specific Language handles more complex Machine Learning workflows and the creation of higher level algorithms
- Tools: integration options with other platforms such as Google Sheets, Alexa, Mac OS X, and Zapier
BigML Education Programs
In addition to new features, our educational programs have also been engines accelerating the pick up in users in 2017. Our resident Machine Learning experts have put together many assets to help aspiring professionals start their learning journey in multiple modalities, e.g., Machine Learning Summer Schools, custom workshops, free subscriptions for active educators and students, online tutorials, partner webinars, and professional certifications. In continuing the trend, BigML’s 3rd edition of the Valencian Summer School in Machine Learning (September 14-15) was upgraded to a larger venue this year in anticipation of record demand.
We’re delighted to bring Machine Learning to more people every day, especially given that the BigML community includes everyone from big name brands to individual users around the world. This remarkable strong growth we have been experiencing further validates the importance of our mission to make Machine Learning accessible for everyone. Here’s to the next 50,000!