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Dotscience is shutting down

dotscience.com

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Re: Dotscience is shutting down

#21

Post by gk1 »

bagrow wrote: post #3

Thoughts on how the economic situation will affect machine learning and data science in industry more generally?

If AI/ML was just part of R&D then it's likely to get downsized or cut as companies refocus on their core products. If AI/ML is or is meant to be powering the core products or services, eg at banks, retailers, and manufacturers, then those teams are actually more important than ever. Because ML usually reduces labor and overhead, so the faster they can get it into production the faster they'll lower their operating expenses.

The challenge for dotscience was that there are so many players in this space that it's hard to stand out and win deals. See: https://zdnet3.cbsistatic.com/hub/i/r/2019/07/17/b17497a0-84... (These landscape charts are always made to look complicated on purpose, but the point is clear.)

Re: Dotscience is shutting down

#22

Post by lewq »

Btw https://mlops.community (which we started) will live on, Demetrios, Chris and Dan are kindly keeping it running ️

If you're interested in MLOps, come and join our weekly 9am PT / 5pm UK Wednesday online meetups - it's a great community and it's growing quickly - and now it's vendor neutral too :)

Re: Dotscience is shutting down

#23

Post by lewq »

streetcat1 wrote: post #7
lewq wrote: post #2

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.

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.

Re: Dotscience is shutting down

#24

Post by lewq »

ThePhysicist wrote: post #11
lewq wrote: post #2

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.

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

Re: Dotscience is shutting down

#25

Post by streetcat1 »

fxtentacle wrote: post #19
antonvs wrote: post #16

Earlier quoted context omitted.

How many other ML people do you work with? Do they all use the same system? Are you only working with Tensorflow and no other tools? Generally things like MLOps are targeted at providing systems for people that either don't know how, or don't have the time, to set something up themselves, especially when the data science team is more than just one or two people, and when they're using a range of tools that need to in…

2 others, and yes, everyone has access to the central git repo and to our shared private Docker repository. And no, we use TensorFlow and Chainer, but both are python frameworks. Plus Numpy, of course. I like that KubeFlow has an introduction video, but I find it quite odd, too. They talk a lot about how they will make things simpler, but then I learn that I'll need to run Kubernetes on my laptop, my servers, and pot…

So ML ops is just part of a solution, which I think is the reason why it is a hard to sell.

What I think might work is auto ml combined with models ops, or rather auto model ops.

Or even better: auto data managmenet -> auto pre-processes -> auto ml -> auto ops.

Re: Dotscience is shutting down

#26

Post by lewq »

wtvanhest wrote: post #6
lewq wrote: post #2

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.

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

Re: Dotscience is shutting down

#27

Post by carlosf »

fxtentacle wrote: post #13
nojito wrote: post #10

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…

MLOps is just a buzzword, but maintaining ML projects in large organizations has several major pains, e.g.:

http://papers.nips.cc/paper/5656-hidden-technical-debt-in-ma...

I use MLFlow at my company for solving two specific problems: tracking experiments and model performance.

But if you're small enough, you probably don't need MLOps, Devops, etc...

Re: Dotscience is shutting down

#28

Post by fxtentacle »

streetcat1 wrote: post #25
fxtentacle wrote: post #19

Earlier quoted context omitted.

2 others, and yes, everyone has access to the central git repo and to our shared private Docker repository. And no, we use TensorFlow and Chainer, but both are python frameworks. Plus Numpy, of course. I like that KubeFlow has an introduction video, but I find it quite odd, too. They talk a lot about how they will make things simpler, but then I learn that I'll need to run Kubernetes on my laptop, my servers, and pot…

So ML ops is just part of a solution, which I think is the reason why it is a hard to sell. What I think might work is auto ml combined with models ops, or rather auto model ops. Or even better: auto data managmenet -> auto pre-processes -> auto ml -> auto ops.

I think a service that rents out GPU-equipped bare metal Ubuntu + Docker servers by the hour would be what I'd want to use.

If they also offer a private Docker repo, fast S3-compatible storage and some pre-built images with Jupyter preinstalled, that might be the entire ML pipeline that I need.

Re: Dotscience is shutting down

#29

Post by TrackerFF »

nojito wrote: post #10

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.

It is tough to make money, unless you have a solid budget for sales - even more so when you need sales people with some degree of competence in the field you're working in, and the product you're selling.

Re: Dotscience is shutting down

#30

Post by streetcat1 »

lewq wrote: post #23
streetcat1 wrote: post #7

Earlier quoted context omitted.

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?

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