
Show HN: Deepserve.ai – Heroku for Deep Learning - jeffrwells
https://www.deepserve.ai/
======
jeffrwells
Hi HN,

I’m Jeff Wells. I am launching
[https://www.deepserve.ai](https://www.deepserve.ai) — a platform to deploy
and host machine learning models.

Deepserve makes it extremely easy to take a trained model and deploy it with a
single CLI command: `deepserve deploy`

We host the model, deploy it, and give you an API endpoint you can call on to
make predictions from your applications. We manage the devops and dependencies
and ensure your model is always running. We’ll scale up as much as your
application needs, and scale down during off-peak hours to save you money over
renting your own servers.

In order to make it easier to use, we have client SDKs to make calling your
model a single line of code. We store all of the production inference data so
you can grow your training sets with real examples.

~~~
jeffrwells
OP here.

We’re now open for beta! I want to onboard companies slowly as it’s critical
our infrastructure is robust. Out of the gate I am supporting Fast.ai and
working hard to support PyTorch and Tensorflow.

If you don’t have the skillset right now to build your own models — reach out
on
[https://www.deepserve.ai/consultancy](https://www.deepserve.ai/consultancy)
or email me. I can build models for you and host them Deepserve. I’ll be
transparent that I want to make money on hosting, and would love to take your
use case and turn it into a case study.

As an example — I have a client on the platform right now using a text
classifier that auto-tags and auto-categorizes their support emails. These
emails then get routed and sorted automatically, with the most urgent cases
alerting them via slack.

—

This has been a side project for over a year, and am finally ready for launch.
Feedback is welcome and I’d love to connect with anyone who aligns with the
mission of bringing machine learning to the world.

You can reach me at jeff @ deepserve.ai

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kparaju
All three of the big cloud providers have a solution for ML model deployment
and support more libraries than just Fast.AI. What are some of the reasons one
would use Deepserve.ai vs. the other cloud providers?

This is really neat though. The fact that this is so easy to use will be a big
appeal to a lot of data scientists who don't want to write production code or
deal with lots of bootstrap configuration. There are also a lot of benefits of
abstracting the deployment as you can seamlessly add a lot of features like
logging, or even make performance improvements by tweaking a few env vars and
everyone will get it by default! Thanks for sharing.

~~~
jeffrwells
Great question!

My goal with Deepserve.ai is to be drastically easier than the big cloud
providers, with less lock-in. Rather than giving you infinite configurability
with low-level primitives, I want to give you an easy, scalable solution right
out of the box. Sagemaker and Tensorflow Serving have some quick-to-deploy
options like this, but are really built in expecting you to use their entire
ecosystem.

I started off with Fast.ai for simplicity and want to expand to support
PyTorch, Tensorflow and ApacheMX. My aim is to have an option that is tested
and proven for every library version and every mix of inputs/outputs. So if
you have a PyTorch 1.5.0 model that expects an image and returns a set of
bounding boxes, you choose that configuration and have everything ready to go.

You're right on with having things like logging and tuning built in for
everyone from the get go.

I will obviously never be cheaper than the big cloud providers. I want to
compete on ease of use and the soft costs of hiring for or dealing with
devops.

Thanks for your kind words!

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p1esk
Hi Jeff, I'm not your target customer, but I could become one if I ever launch
my own startup. So I'm curious about:

What's your background? How did you come up with this idea? How did you get
started? How did you get funding? How do you market this product (other than
posting on HN)? How much do you work on it (what's your day like)? When do you
plan to become profitable? Are you worried about competition (e.g. Amazon
offering an easy way to deploy a model)?

~~~
jeffrwells
I'd love to share, thanks!

My background is as an application developer (RoR, React, Python) and fell in
love with machine learning. I wanted to use ML at the startups I worked at but
with everything involved it would become a bigger project than anticipated and
fall out of priority.

The aim with Deepserve.ai is to make shorten the cycle enough that you could
train and deploy a prototype model in a day and a production ready model in a
week (if you had the data ready).

I am actually still part time and bootstrapped — I spend a few hours a day
hacking on the platform and am starting to market to ML practitioners and
sales into enterprise. It's definitely a lot of context switching — from React
to CLI writing to AWS devops to marketing.

My aim is to be profitable out of the gate by having pricing with sufficient
margin over my underlying compute cost. I'll definitely be more expensive than
doing it yourself — but the point is that you don't have to do it.

If you launch your startup, definitely reach out, I'd love to add AI to your
product! And if you want any advice on your startup, I'd love to connect, this
is my third startup and I've had a couple of successful exits so might have
some ideas!

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panabee
which gpus do you use? is the pricing ($1 per 1000 requests) independent of
inference time and bandwidth? for instance, some of our models finish within 2
seconds while other models take ~60 seconds, depending on the input. we have
been searching for something like this for a long time, but all the other
options were lacking in one way or another.

~~~
jeffrwells
Hey panabee, pricing is definitely something I want to iterate on. I wanted to
start off simple and then move to tiered pricing based on what type of model
you're running, whether you need CPU/GPU and tiers of execution time. Let's
definitely connect as I'd love to serve your use case

