Making a Commercial Success of Machine Learning: How to Create Value – 2018 Report
The “Making a Commercial Success of Machine Learning: How to create value” report has been added to offering.
“Making a Commercial Success of Machine Learning: How to create value”
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.