Ask HN: How to Make Money from Machine Learning?
I am currently learning Machine Learning and I am curious as to how I can use what I am learning for commercial purposes.
In my opinion, there are three business "tiers" of ML.
1. Process automation. You're turning a defined business process currently done by a human into something automated, with some custom rule logic, of which some of it may be via ML-trained models. The easiest, because the criteria are well-defined and everyone knows what success looks like.
2. Data Mining/Analysis/Insight. Your company sits on some unexploited set of data, and you want to generate useful business insight from it. ML models can help make sense of it. This takes the traditional business intelligence function to the next level. Harder, because you may need to educate the company on what ML offers. They may not even realize what types of new questions ML can answer.
3. Customer-facing automated-decision services. This is the most demanding application from a business perspective, but not necessarily from a technical perspective. The standards for quality, stability, accuracy, should be much, much higher. If it's customer-facing, it can't mess up, or people will stop trusting it. The customer may be internal or external.
Literally millions of people have already trod this path, the low-hanging fruit in finance is already picked, and in many other contexts as well. The big mistake people new to the field make is to reason about machine learning as if it's a replacement for human intelligence, rather than just applied large scale statistics.
Do you know some other area extremely well, where you could apply these methods? Given how vague your question is, it sounds like you don't have much background or sense of direction. You will get taken to the cleaners if you try to compete with an established group; if you have some specialized context in which you can use machine learning, by all means, but the short answer is that you can't make money with machine learning. You may be able to find a problem or an application you can improve with machine learning, and make money that way. But it's very easy to lie to yourself in your evaluations with methodological errors, and then ship a product that does nothing remotely like what you intended / evaluated it for.
The two types of applications I've seen so far generate real value (and thus have monetary value where you can actually earn) are: - Automate existing processes to either reduce the amount of work needed to be done (feature extraction from images or audio, document parsing) or to introduce a higher level of resolution/response time. For example forecasts for the next day every day or live detection of audio/visual events.
- Generating models to extract signals from massive or complex data. Usually once you're done here you can revert to traditional methods based on the newfound insights. Rarely is there "magic" solutions to optimize away your problems. In general it proves more to be a tool in the box to do analytics than it is a solution.
Either of those create new value and you can put a price on it. There's decent opportunity once you understand what are appropriate use cases.
I foresee uses for these new capabilities in communication services, IoT devices, user interfaces (esp language based), social communication tools, security, reliability, surveillance, and even some far out cutting edge stuff like personalization, GUI optimization, error correction, and restyling content and games and media to better match your likes and dislikes.
most technologists including ML engineers are more interested in how the latest, greatest kernel provides a 0.04 higher F score on some academic facial recognition task than they are understanding problem spaces, understanding what problem spaces would benefit from "ML" (read: data + statistics), then providing a solution that people value enough in order to pay for (this includes sales, marketing, etc.).
or just work for a FAANG, probably easier and over time a wiser investment
Or predict and trade bitcoin.