How do you develop a business connection and market your service as being more valuable that the wide abundance of free materials, engineering blogs, research papers, etc., in this space?
Just for example, I run a team of 10 machine learning engineers at a large ecommerce company. We mostly do NLP and computer vision, some time series forecasting.
I cannot imagine ever paying anything close to $25k for consulting advice, that’s just bananas to me. We recently purchased licenses to use the data annotation tool prodigy from the spaCy creators at explosion.ai. That was ~$4000 and the decision whether to build our own data annotation system or not was excruciating, involved all kinds of business documentation, RFCs, approvals, NDA processes, etc. It was deeply non-trivial to procure that, and building our own was a very serious option we pursued with tech specs and prototypes and everything.
Spending 6x that amount for _advice_ about NLP, which practically grows on trees today, is just totally unrealistic.
It makes me suspect the real target customer for you is not companies with actual ML engineering teams or ambitious data-driven projects, but more like someone looking for McKinsey-lite. Some place that has no serious ML use case beyond drop-in pretrained models and sees $25k as the cheaper path to rubber stamp certification that dissolves internal political feuds. Most likely just selling super cookie cutter NLP models as if they are advanced and represent some sexy leap forward for a company with a couple junior data scientists. Algolia or just some drop-in Elastic tfidf search is more than enough for these companies. Spend the $25k on an intern who can tell you anything you need to know about neural network frameworks.
In reality, the 4-5 ML engineers you already hired are very likely more knowledgeable than the freelance consultant you might hire. They can tell you much more about state of the art and simultaneously know the specific integration path in your company’s web service and data ecosystem. Those folks won’t be wasting time prototyping - they would be pursuing a more efficient way to get the answers you need than advice from a freelancer, even if that freelancer was Bengio for pete’s sake.
I just cannot see the value prop here except for the usual story of paying for consulting as a virtue signal / credential / politics kind of thing.
I just really don't know what to tell you, I'm sorry. We saw the value, pursued the person, and had an engagement with them effectively consulting for 50 hours. There was no justification to anyone outside of our team, other than getting the very quick approval, and the consultant worked with our team directly and not to anyone outside the team. There was no internal politics at play.
And again, this wasn't a "freelance consultant" in the sense of the original story posted to HN, but an accomplished person at a very respected company who was able to secure the approvals from their side to help us out. This was novel/niche work for which "drop in pretrained models" don't really exist or apply.
Frankly to judge and belittle someone else who is just sharing their experience by saying they're not doing serious work and don't have serious customers is very rude.
It’s not rude at all to highlight how this warrants huge skepticism. “Just calling someone up” to spend $25k+ on “advice” and claiming it’s justified in time saved of your existing staff not flailing around to research the same topic is _very_ weird. That doesn’t line up with any professional experience I’ve ever seen in any machine learning team at Fortune 500, startup, finance & academia jobs. _Maybe_ for appearance fees to have a big name researcher come and give a company presentation or keynote. _Maybe_. Absolutely not believing it for ad hoc freelance consulting based on someone having some NLP projects on GitHub, conference presentations or publications, or gigs at “fancy” AI employers, and I find it’s worth making this comment so other readers can consider how extremely shaky that premise is.
Just to provide another perspective : I've consulted for 20+ companies. Spending money for outside advice or actual work products is extremely common and generally worthwhile if done right. There's a reason why consulting is a multi billion dollar industry.
I disagree. Consulting is fashion signaling. It’s a huge business because it’s about buying and selling political status in the context of company decision making. It offers value from social signaling, not from strategic facts that demonstrably yield improvements to success metrics.
>That was ~$4000 and the decision whether to build our own data annotation system or not was excruciating, involved all kinds of business documentation, RFCs, approvals, NDA processes, etc. It was deeply non-trivial to procure that, and building our own was a very serious option we pursued with tech specs and prototypes and everything.
The fact that you went through all of that for a 4k line item means there's something wrong at your end. You would have burned through multiples of that putting together a prototype, only to them spend lots of hours on the procurement process for my team would have just put on a credit card (exaggerating but only somewhat).
Sounds like you are not really accounting for the opportunity cost of your peoples time.
> You would have burned through multiples of that putting together a prototype
No, we had the prototype we needed for making the decision with 1 team member working on it part-time for 4 weeks.
Your economics are way off. The cost of the prototype pales in comparison to risks around vendor lock-in, security issues, license growth, etc. The legal team vetting the contract and NDAs is the real expense - and well worth it to be quite careful about vendor software.
The cheap quick prototype helped us realize the longterm cost of maintaining that tool was too large, and the upfront procurement costs were worth it.
In a “move fast and break things” shitshow where you just instantly buy the vendor software, the risk of getting burned on a bad / unsafe contract is huge, and you end up playing hot potato with the 3 licenses you bought because you didn’t appropriately plan for license growth, dealing with data breaches, etc.
I think you’re naively reacting to the perception of a bureaucracy unable to do anything, but it’s totally not. This type of vetting is very cost effective.
It’s also why nobody is just quickly dropping $25k on consulting from freelancers.
I work at an f500, and I have worked in other data science groups at companies in the same industry. It's no huge thing for a large consultancy to be thrown 500k-3M for a 6 month "predictive" project whose deliverables are some python code that trains simple scikit models.
Absolutely grinds my gears too because the consultants turn in dreck that we're expected to fix to help the VP who hired them save face.
Not sure why I'm sharing except that the last line of your comment really hit home for me.
> I cannot imagine ever paying anything close to $25k for consulting advice, that’s just bananas to me
The parent already explained why he was willing to pay $25k for a consultant, but you ignored his explanation and just started talking right past him and insulting the work he's doing.
" It makes so much more sense to pay $25k to get direct knowledge of systems and techniques versus your team spending a bunch of time exploring different products/methods/algos to find something that might work in the end, or might fail in a few months. $25k is like 5 MacBooks, hardly worth thinking about versus being able to get experienced direction from someone who has done what you're trying to do and saving your team literally hundreds of hours of exploratory work."
>Spending 6x that amount for _advice_ about NLP, which practically grows on trees today, is just totally unrealistic.
If you can't conceive of what useful advice might look like then that's just a failure of imagination on your part. Getting any ML model to work properly involves a lot of esoteric domain-specific tricks. You have to use A,B,C to model the problem domain, apply X to transform the data, clean the data by throwing out Y, impose constraints Z on the model. You can pay your full-time employees hundreds of thousands of dollars to spend hundreds of hours discovering these through trial and error, or you can just pay someone $25k to explain these to you up front.
Of course you can find a billion worthless, generic, baby's first bag-of-words spam filter type NLP tutorials online, but that's not the same as commercially applicable expertise.
Just for example, I run a team of 10 machine learning engineers at a large ecommerce company. We mostly do NLP and computer vision, some time series forecasting.
I cannot imagine ever paying anything close to $25k for consulting advice, that’s just bananas to me. We recently purchased licenses to use the data annotation tool prodigy from the spaCy creators at explosion.ai. That was ~$4000 and the decision whether to build our own data annotation system or not was excruciating, involved all kinds of business documentation, RFCs, approvals, NDA processes, etc. It was deeply non-trivial to procure that, and building our own was a very serious option we pursued with tech specs and prototypes and everything.
Spending 6x that amount for _advice_ about NLP, which practically grows on trees today, is just totally unrealistic.
It makes me suspect the real target customer for you is not companies with actual ML engineering teams or ambitious data-driven projects, but more like someone looking for McKinsey-lite. Some place that has no serious ML use case beyond drop-in pretrained models and sees $25k as the cheaper path to rubber stamp certification that dissolves internal political feuds. Most likely just selling super cookie cutter NLP models as if they are advanced and represent some sexy leap forward for a company with a couple junior data scientists. Algolia or just some drop-in Elastic tfidf search is more than enough for these companies. Spend the $25k on an intern who can tell you anything you need to know about neural network frameworks.
In reality, the 4-5 ML engineers you already hired are very likely more knowledgeable than the freelance consultant you might hire. They can tell you much more about state of the art and simultaneously know the specific integration path in your company’s web service and data ecosystem. Those folks won’t be wasting time prototyping - they would be pursuing a more efficient way to get the answers you need than advice from a freelancer, even if that freelancer was Bengio for pete’s sake.
I just cannot see the value prop here except for the usual story of paying for consulting as a virtue signal / credential / politics kind of thing.