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Comment by lewq | Hacker News Reader

CEO here. Happy to make introductions to the team for anyone who is hiring. We have valuable residual knowledge about building a product in the MLOps platform space and achieving product-market fit, and I hope that this might be helpful to someone else.

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I cannot imagine shutting down a company with a team. Must be really hard. Your note was refreshingly succinct, with genuine thank yous to the people that helped.

I don't have any advice or anything, just wanted to let you know that anyone that has ever tried something and failed understands how painful it is, and everyone else won't. Best of luck to you and your team as you all move forward.

Thank you

Sorry to hear this! Can you share what went wrong ? I’m really interested in the “DevOps for ML” space and I would love to hear what you think was the greatest difficulty or problem with your approach (if you are willing to share that).

Happy to talk - please email me - luke@dotscience.com

Certainly have them check out the WiMLDS job board for positions that may fit their expertise, or contact SigOpt since they seem to be in the same space. I'm not affiliated with either, but know some good folks from both.

Thank you.

So I am about to introduce a new product into this space, can you share how did you get your first 1-50 customers?

Also, Can you share general details about your customer demographic? I.e. what verticals, company size, etc?

Direct outreach on LinkedIn and conferences. Fintech & insurance tech was beachhead market, followed by finance.

Thank you for your answer.

Another question: can you elaborate more on how did you price the product? Did you do A/B testing on the price? ask the first customers?

We priced too low at first, tried too high when we released our enterprise product, did some compares with other dev tooling in sales convos with early customers and then settled on something in the middle ($600/user/month) and stuck with that cuz it seemed to be working. If we'd been around for longer we would have done more pricing tests but getting the right features to unlock larger deals (more users) was higher priority at the end. Btw, the delivery model that had PMF was customer hosts it in their own cloud, we ship terraform

Thanks again for the info. This is what I thought about self-hosting.

However, did customers raise concerns about hosting your code in their on perm clusters, For example, did they insist that you open source?

Also, when you host the code on the customer cluster, how did you track usage? For example, was there a process within the customer site, that would send usage information to your site ?

And, I would also imagine that every engagement is unique (not sure if this is correct),

Hence, did you outsource the solutions engineering or that was done as part of the team?

Hi, one of the engineers who created dotscience here. Customers didn't insist on open sourcing anything, we did have some open source components but I don't think they cared about that :) Regarding tracking usage - usually it was just many conversations with customers and having them on our Slack channel. Solutions engineering - mostly development team would be helping with writing anything specific that they need. On the SaaS side we had a lot more analytics, used segment, intercom and internal "audit events" to better understand what's happening.

Thanks much for the response.

So to sum up, what did customers care about the most from your point of view? (maybe top 3)

1. Deploying models easily (data scientist doesn't need to grok docker/kube)

2. Monitoring models including data and model drift/statistical monitoring (data scientists don't need to grok prom/grafana)

3. Only once these base concerns are met, model inventory, provenance, data versioning, reproducibility, collaboration with notebooks, ci integration etc.

Happy to talk more - drop me a note at luke@dotscience.com

You'd make a lot of AI/ML developers happy by open sourcing as much as possible from your product.

Leave a legacy, maybe the community will keep it going!

I'd love to, genuinely, but I don't think it will be possible. https://github.com/dotmesh-io/dotmesh is already open source though which is one key component of Dotscience.

Were you making money but wasn't enough for growth requirements based on existing funding or did you just not have a viable business model ?

We had early revenue and healthy pipeline, we just ran out of that crucial currency: investor patience.

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