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This is clickbait (1) to promote his startup, Proda. ML is used in business workflows all the time - to date, I have built several solutions that are being used for 53 clients, internal and external. Here is what makes B2B ML hard: People have to trust it. This isn't some movie-recommendation engine, which spams you with more bank heist movies after you watch one. B2C ML systems can get it wrong, and customers are generally forgiving, because it's a low stakes game. B2B applications are generally higher-stakes, because they impact business workflows, and if someone has decided to automate it, it's probably a high-volume, critical workflow. It has to be extremely accurate, and demonstrably better than the equivalent human system. The problem has to be well-defined enough that an ML system can act with high-accuracy, but not well-defined enough that a rule-system could replace it. Don't use ML if a rule-system will do a better job. (For those scenarios, you can still put an ML anomaly-detection system to make sure the rule-system is still valid, and to guard against data input changes.) As just mentioned, the problem also has to be important enough and high-volume enough to warrant an ML solution. The percentage of problems that fulfill these criteria is not very large. Now to actual ML development and deployment - the model is the tip of the iceberg. The rest of the iceberg is data acquisition, feature selection, data/feature versioning, automated training, CI/CD, model performance monitoring, et cetera. If ML is being developed inside a software development organization, this isn't a problem, most people will understand this. If it is being developed within an embedded BI team inside a business unit - they will generally not have support/runway needed to build the full system. The ML model might make it to production, but it will probably run naked, be brittle, and hard to retrain. A dramatic failure with business impact is just a matter of time. There are a lot of low-code, no-code ML solutions that have been developed, or are being developed, and some of the supporting infrastructure as well, but, at the risk of sounding parochial/protectionist, you need a rock-solid, end-to-end, integrated, data management system that is fully understood by whomever needs to pick up the phone at 2AM. It's the interfaces that are hard, and chaining together a bunch of third-party black-box systems just means more interfaces and behavior you don't control. Choose and use these systems wisely. So yeah, B2B ML is hard. But it's generally not due to lack of data, and transfer learning is generally not necessary. Understanding business processes is important, I agree, but that's comparatively easy. It's what consultants have been doing for decades. The hard part is choosing a problem where ML can add value, and then executing on it with enough integrity that people will actually trust it. (1) Ok, clickbait might be harsh. But it is self-promotion, and the article itself is a collection of generic banalities. I feel it falls on the wrong side of the line. |