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Making a Commercial Success of Machine Learning: How to Create Value - 2018 Report - ResearchAndMarkets.com

November 30, 2018

DUBLIN--(BUSINESS WIRE)--Nov 30, 2018--The “Making a Commercial Success of Machine Learning: How to create value” report has been added to ResearchAndMarkets.com’s offering.

In recent years, investment in ‘pure-play’ machine learning (ML) has taken off.

Four facts stand out:

1. Twenty-five times more ML businesses in the UK raised Seed and Series A funds in 2017 than in 2013.

2. The median amount raised by these businesses increased significantly.

3. During the same period, the mean valuation of Seed and Series A pure-play ML businesses - enterprises that only develop ML solutions - increased at a compound annual growth rate (CAGR) of 16.6%.

4. Revenues for ML-as-a-service (MLaaS) are anticipated to grow at a CAGR of in excess of 40% through the next the five years.

These high-level numbers, although compelling, are simply the aggregate result of the answer to two main questions:

How are ML businesses valuable? What is the source of their value?

This report provides a response to both.

By considering such insight, and its conclusions, those running ML businesses can adjust their strategy to maximise shareholder returns, and those investing in these enterprises can conduct commercial due diligence and negotiations with confidence. Lastly, potential victims of ML solutions can reflect on how their businesses and industries should respond.

A machine learning solution is valuable, first and foremost, because it has sufficient predictive power. Predictive power is the ability to anticipate future events and is the consequence of machine learning. Without it, there is no ML solution, and therefore nothing of value.

To create predictive power an ML business needs three key resources - the right people, adequate training data (from which an ML algorithm learns), and significant computing power - all focusing on applying the appropriate method.

Data scientists are the most important people in the process, and serve three primary purposes:

1. Selecting the appropriate ML method.

2. Establishing and improving predictive power.

3. Building the initial solution, based on data appropriate to the problem.

An ML solution becomes truly valuable when its predictive power exceeds 90%. Every gain made in the 90-100% bracket can be extremely challenging to achieve, but will serve to cement trustworthiness and uniqueness. Data scientists are responsible for this.

Two other groups of people are involved at the creation phase: the creator (usually the founder), and software engineers. The creator is mostly responsible for ensuring that the solution continues to match the problem.

Software engineers concentrate on creating the medium for delivering the solution - the platform, user experience (UX) and ability of the users to integrate/utilise its predictive power.

Reflecting the focus on people, the dominant investor in UK ML businesses at the Seed stage, Entrepreneur First, makes its investments about providing the right, high-quality individuals with the agency and time to create ML solutions.

The richness, maturity and predictive power of an ML solution cannot be easily replicated. Nor can capital invested in a competitor necessarily recreate it. Which is why predictive power is central to value; it’s what makes the solution unique, and why ongoing development to ensure consistent accuracy and relevance is key.

Although people are central to product creation, it is because of improvements in the availability of training data and computing power that ML solutions have proliferated over recent years. Data inputs from the Internet of Things (IoT) and the ever-increasing digitisation of life mean that 2.5 exabytes of data are now created daily, equivalent to 90 years of HD video.

A further stimulus for solution development has been the release of ML best practice, example algorithms and modelling frameworks to the public by leading technology companies such as Google, Facebook, and IBM (the first to do so in 2015).

As the barriers have come down, pure-play ML businesses have become more common. No longer are solutions and expertise concentrated in the hands of a few major technology companies.

Potential money multiples in excess of 7x would not be an unusual expectation

To secure the necessary capital to create an ML solution, businesses typically raise funds through the sale of equity. Based on the limited available data, it would not be unusual for those that invest at the first Seed phase to expect a 7x return on their money.

This expectation will influence how aggressive the business needs to be, and is also the expectations bar that the business will need to reach to secure funds in the first place. In simple terms, the product/market fit, future value opportunity, leadership team, and so on, must convince the investor that this level of return is possible.

Companies Featured

Alphabet Inc. Amadeus Capital Amazon Apple Inc. Facebook Inc. FiveAI Microsoft Prowler.io Senseye Storybricks Tay.ai

For more information about this report visit https://www.researchandmarkets.com/research/rlbd57/making_a?w=4

View source version on businesswire.com:https://www.businesswire.com/news/home/20181130005298/en/

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Related Topics:Machine Learning and Data Mining

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SOURCE: Research and Markets

Copyright Business Wire 2018.

PUB: 11/30/2018 10:21 AM/DISC: 11/30/2018 10:21 AM

http://www.businesswire.com/news/home/20181130005298/en

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