The Ghost Olympic Event: Machine Learning Startup Acquisition

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With no lack of drama both on and off the track, the 31st Summer Olympics and its 39 events have been wrapped up recently. As the city of Rio is preparing for the first Paralympic Games to take place in the Southern Hemisphere, some are experiencing Synchronized Swimming, Canoe Slalom and Modern Pentathlon withdrawal symptoms.  As Usain Bolt, Michael Phelps and Simone Biles stole the show, Silicon Valley has not just quietly sat and watched the proceedings. Not at all.  In fact, VCs, investment bankers and tech giants active in the Machine Learning space have been in a race of their own that goes on unabated even if they don’t get the benefit of prime time NBC TV coverage.

olimpic inverse musical chairs

Machine Learning as strategic weapon

It is fair to say that we have been witnessing the unfolding of the ghost Olympic event of Machine Learning startup acquisition.  The business community’s scores are not fully revealed yet and the acquisition amounts are mostly being kept under the wraps — albeit in a leaky kind of way.  Regardless, the most recent acquirers include Apple acquiring Turi and Gliimpse, Salesforce purchasing BeyondCore, Intel picking up Nervana Systems, and Genee being scooped up by Microsoft. So what is driving this recent surge?

The bulk of the M&A activity have been led by household B2C names like Google, Apple and Facebook that are sitting on top of piles of consumer data that can result in a new level of innovation when coupled with existing as well as emerging Machine Learning techniques like Deep Learning.  The dearth of talent to make this opportunity a reality has resulted in a very uneven distribution of the said talent as those deep pocketed “acquihirers” outbid other suitors to the tune of $10M per FTE  for early stage startups (and even higher in the case of accomplished academic brains).

The emerging need for a platform approach

As great as having some of the brightest minds work on complex problems is, it is no guarantee of success without the right tools and processes to maximize the collaboration with and the learning among developers, analysts and resident subject-matter experts.  Indeed, the best way to scale and amplify the impact from the efforts of these highly capable, centralized yet still relatively tiny teams is adopting a Machine Learning platform approach.

It turns out that those that started on the path of prioritizing Machine Learning as a key innovation enabler early on already have poured countless developer man-years into building their own platforms from scratch. Facebook’s FbLearner Flow, which the company recently revealed is a great example of this trend. As of now the platform claims to have supported over 1 million modeling experiments conducted to date, which make 6 million predictions per second possible for various Facebook modules such as the news feed. But perhaps the most impressive statistic is that 25% of Facebook engineers have become users of the platform over the years. This is very much in line with Google’s current efforts to train more developers to help themselves when it comes to building Machine Learning powered smart features and applications.

Machine Learning haves (1%) and have nots (99%)

Examples like the above are inspirational, but this brings the question how many companies can realistically afford to build their own platform from scratch. The short answer is “Not too many!”

Left to their own devices, these firms face the following options:

  • Hiring few Data Scientists that may each bring their own open source tools and libraries of varying levels of complexity potentially limiting the adoption of Machine Learning in other functions of the organization, where the ownership of mission critical applications and core industry expertise reside.

  • Turn to commercial point solution providers with a few built in blackbox Machine Learning driven use cases per function e.g., HR, Marketing, Sales etc.

  • Count on the larger B2B players’ recently launched Machine Learning platforms to catch up and mature in a way that can not only engage highly experienced Machine Learning specialists, but also serve the needs of developers and analysts alike e.g., IBM, Microsoft (Azure), Amazon (AWS) etc.

Although these options may be acceptable ways to dip your toes in the water or stop the bleeding in going to market with a very specific use, they are not satisfactory longer term approaches that strike the optimal balance between time to market, return on investment and a collaborative transformation that leads to a data driven culture of continuous innovation that transcends what can be achieved with small teams of PhDs. As a result, despite the recent advances in data collection, storage and processing, we are stuck with a data rich but insights (and business outcomes) poor environment awash with a cacophony of buzzwords in many industries.

Luckily, there’s still an incipient industry of independent Machine Learning platforms like BigML, H2O and Skytree (no more Turi) that can supply this unfulfilled demand from the so far lagging 99%. However, we must remember that replacing those platforms with new complete ones may require years of arduous work by highly specialized teams, which runs counter to the present day two co-founder, Silicon Valley accelerator startup recipe targeting a quick exit despite little to no Intellectual Property.

Regardless if any tech bellwether is able to create a monopoly, it seems safe to assume that for the foreseeable future the race for Machine Learning talent is only going to get hotter as more companies get a taste of its value. We will all see whether this game of inverse musical chairs will lasts long enough to make it to the official program of Tokyo 2020!


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