It’s not every day that one comes across a commercial software platform hitting the 100,000 registrations mark in the Machine Learning world. After all, Machine Learning is only now shedding its reputation as mostly an academic endeavor and becoming a business imperative for both large and mid-sized (or even small businesses) that represent many industries and are looking to implement a wide variety of use cases.
In the case of BigML, it took about 6 years to get to our first 50,000 registrations starting from our inception in 2011. However, it has taken less than 2 years to add the next 50,000, which is a testament to BigML’s staying power despite the existence of a dizzying array of Machine Learning tools including highly specialized open source tools and libraries.
Naturally, one wonders what forces are driving the accelerating adoption of BigML given our recent experience. As you’d expect, while some reasons are exogenous, others are endogenous to BigML’s product design and go-to-market choices. With that stated, the following waves of change come to the fore:
- Without a doubt, the interest in Machine Learning has seen an exponential increase in the business world. The routine mentions of “Machine Learning” and/or “AI” in public company earning calls by many executives demonstrate how related initiatives are perceived to introduce strategic implications for many industries.
- The BigML platform has continually evolved and improved over the course of the last two years making it more comprehensive and able to handle many diverse use cases once out of its reach. It’s somewhat nostalgic to remember that the first version of BigML only featured decision trees as part of a very simple workflow that supported flat 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.
- We must give a special attribution to our auto-ML capability OptiML, which has leveled the playing field for even the novices not familiar with the intricacies of hyperparameter tuning by automating the chore of picking just the best set of parameters for any classification or regression technique available on the platform. More competent models, in turn, mean higher potential business impact and even more interest in iterating with better features, more data, etc. Before you know it, it becomes a positive feedback loop!
- Despite adding tens of new capabilities since 2017, we’ve managed to do so without compromising the initial promise of making Machine Learning easy and beautiful for everyone. The core flow and architectural design of the BigML Dashboard and API have remained the same, only adding more versatility through new algorithms and resources added to the existing menus. We’re no fans of Frankensteinian bolt-on jobs or stovepipe, save-the-day integrations.
- Our insistence on skipping online forms to fill and sales calls to have before an interested party can even get to experience BigML has also been paying off handsomely. We like to refer to this as Free and Immediate Access as there are no large downloads or painful setup or installation routines or worse yet credit card verifications needed to actually tackle your first predictive use case. Just enter your email and kickstart your personal Machine Learning journey.
- The next factor is what we can sum up as the human touch aspect and it includes a mix of affordable summer schools, certifications, timely customer support provided to both paying and non-paying users as well as customized assistance that is tailored to desired predictive use cases and ML techniques.
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So regardless of your level of understanding of Machine Learning or the sophistication at your workplace about the matter, you have a spectrum of options to engage with the BigML platform to get real value in the shortest amount of time. We suggest you try any or all of the following routes as your first step and don’t hesitate to reach out to us anytime.
- FREE Forever Subscription: If you haven’t done so there’s never a better time than now to sign up for the FREE version of BigML. It only takes an email.
- FREE Education videos: Unlike the typical advice on how to become a Data Scientist (HINT: take many online courses, read many books on statistics, etc.) you can find a comprehensive set of education videos on each BigML resource that assumes no prior Machine Learning background.
- BigML Lite for Small Business or Pilot Projects: Larger businesses usually require their own dedicated instance of BigML due to internal rules or preferences but for SMBs or a single business unit of a large organization, it makes more sense to deploy BigML Lite for cost and speed to market reasons.
As the BigML Team, we’re proud to serve our community of early adopters and wish to add another 100,000 users in the next year!