11–20 of 46 posts
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.
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'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.
- 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 is coordinated using a git repo which has some shell scripts to execute the correct model with matching data on a compatible TF image. So if someone says "I need you to re-run last week's model", I check out the script from that time and run it on an empty server.
I honestly don't get what MLOps is or why I would need it.
I appreciate how this was handled on the homepage: direct and looking out for your team.
What do you mean by this? I don't see any indication of them shutting down on the homepage, only on the blog.
I appreciate how this was handled on the homepage: direct and looking out for your team.
> on the homepage What do you mean by this? I don't see any indication of them shutting down on the homepage, only on the blog.
> Dotscience
> Thanks for checking out dotscience, unfortunately due to the current situation we have had to close our doors and are no longer operating. You may however enjoy checking out the MLOps community to find out more about all the tooling and best practices happening right now in this ever changing landscape.
> Here are some relevant links to the MLOps community: Slack, Youtube, Weekly meetups.
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…
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 interoperate in various ways.
Something like Kubeflow, for example, does a lot more than what you describe.
Earlier quoted context omitted.
> on the homepage What do you mean by this? I don't see any indication of them shutting down on the homepage, only on the blog.
I got this popup from their homepage: > Dotscience > Thanks for checking out dotscience, unfortunately due to the current situation we have had to close our doors and are no longer operating. You may however enjoy checking out the MLOps community to find out more about all the tooling and best practices happening right now in this ever changing landscape. > Here are some relevant links to the MLOps community: Slack,…
Thoughts on how the economic situation will affect machine learning and data science in industry more generally?
Earlier quoted context omitted.
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…
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…
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 potentially the cloud.
Plus they use irritating marketing phrases like "you can just focus on your model" or "let KubeFlow handle the abstraction of running on X". Most deep learning models nowadays are memory-limited, so improving the model may well mean optimizing GPU memory usage. And after trying to port a working model from Ubuntu + 1080 TI to Google Cloud + V100 and/or Google Cloud TPU, I distrust anyone who would treat those significant hardware differences as "just an abstraction".
So at the very least, I'll need to enforce consistent GPUs and Operating Systems among all servers, just to make things run OK everywhere.
Earlier quoted context omitted.
I got this popup from their homepage: > Dotscience > Thanks for checking out dotscience, unfortunately due to the current situation we have had to close our doors and are no longer operating. You may however enjoy checking out the MLOps community to find out more about all the tooling and best practices happening right now in this ever changing landscape. > Here are some relevant links to the MLOps community: Slack,…
Huh, that's interesting. I even tried a private browsing window without any form of blocking, but I don't get a popup at all.