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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.
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.
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 ?
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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?
Earlier quoted context omitted.
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…
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?
It's really revealing that such amazing companies on paper...just don't make money at all and have to resort constantly to outside money to stay alive.
Your comment is worded in a slightly mean way, but I don't disagree. I work on autonomous 3D navigation. Clicking on their "Autonomous Vehicles" solution I see: - model management solutions - enormous quantities of data Well, I have docker containers for my data, and docker containers for the underlying TensorFlow versions, and docker containers for model source code that went into large-scale training. All of that i…
Having your model in docker generated from git is a good first step, but does not solve the most important issues when you are at scale: reproducible training, tracking of experiments including data, ML-specific observability for your models in prod, etc. See e.g. https://martinfowler.com/articles/cd4ml.html.
More concretely, since the above easily sounds like a buzzword soup:
1. Experiment-wise, you don't want just want to track your ML model definition, but also track the data and everything else used to build it, not just use it. A typical thing I have seen at every company I worked at: we have this model but we don't know how to reproduce it because the lost the data, or there was some magic numbers in training that may be on an internal wiki if you're lucky.
2. For some important use cases, you want to iteratively work on improving the model in production. That often means work on the data side, feature engineering, tracking skew prod vs training, etc. the model is not often changed. In almost every case, a useful model is a model that sees 100s if not more iterations in production. You need 1. to do 2.
3. Some of those tools are useful to enforce invariant or detect data issues, which is again very common when you run models for a long time. See e.g. tfdev.
But to go back to your point: MLOps is a 2nd order kind of thing. The first order is of course that most ML-related projects are useless, poorly conceived, or even lack any kind of quantitative analysis on the business and/or product. Companies should invest there before MLops IMO.
Earlier quoted context omitted.
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…
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 imagi…
How will this affect dotmesh?
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.
Leave a legacy, maybe the community will keep it going!
Earlier quoted context omitted.
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 imagi…
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. O…
So to sum up, what did customers care about the most from your point of view? (maybe top 3)
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.
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!