MARK MIRCHANDANI: On this episode,
we talked to doc.ai about how they're
using Google Cloud for their applications, chat
bots, and machine learning.
So can you tell me a little bit more
about what you actually use inside of Google Cloud,
for instance, the machine learning and any other products
that you might use?
AKSHAY SHARMA: Definitely.
So a lot of what we build on the cloud
is mostly on the Google Cloud.
So we use many different services that Google offers.
We try to be as Cloud Native as we can.
But there are some important aspects
of Google Cloud that makes our job easier
and gets to market sooner.
So largely, our ecosystem of tools that we use on Google
Cloud-- so we use GKE, the Kubernetes Engine.
It's a fantastic product from Google, in my opinion.
We build a lot of microservices that are based on Docker.
And then we are able to rule that out
on GKE as multiple nodes and pods.
And it brings in autoscaling aspects
for us, which is fantastic.
So a lot of our data collection microservices
are basically collecting data across different apps
and users, but also from our partners.
So one line of business that doc.ai has is,
we have licensed these technologies to our partners.
And they have millions of users, from which we
are able to collect that data that's coming in.
So all the data that's collected by
these different microservices, which has burstable capacity,
are actually stored on GCS, the Google Cloud Storage.
So it's important for us to understand
that Cloud Storage provides the reliability that we need,
because then we can use multiple clouds, if we need,
because it becomes a single point of entry for us
to share data across different clouds.
We also use many of the databases that we have.
We use, obviously, Cloud SQL proxy,
which makes the job of being able to connect
to different services really incredibly simple for us.
The other thing that we have done well on Google Cloud
is be able to build parallelization,
so that we can train many, many jobs concurrently
or simultaneously, so to speak, so we
can experiment with different hyper-parameters.
We use BigQuery for a lot of our data analytics downstream.
So based on how users are collecting information,
especially around events and other aspects,
we can throw it at BigQuery and see
what are some interesting correlations that we
can see there.
Most of our machine-learning models
are built today using TensorFlow.
We have found that to be production-ready from get-go,
which is something we are very happy about.
And I think it's also easy to hire today,
relatively in the world of AI and deep learning--
many of them are actually being trained
on TensorFlow, which is a good thing, with the help of what
Google is doing across the ecosystem.
We also use some of the other specific hyper-specialized AI
modules you have.
For example, Google Vision API is
something we use for extracting certain information
in the medical context, specifically around pill
bottles and bounding boxes, and things like that.
MARK MIRCHANDANI: So you also mentioned NLP a little while
ago.
And then earlier, you talked about the chat bots.
How are users using chat bots?
And what is doc.ai doing with those?
AKSHAY SHARMA: It's a great question.
So one thing I did not mention is that doc.ai also
has several chat bots.
So we have a direct-to-consumer chat bot,
which is for mental health.
So we have more than 600,000 users on the platform.
It's a chat bot that runs off of Facebook Messenger.
What we have seen, the chat bots is a very interesting
phenomena.
Because for certain types of diseases,
patients prefer talking to a bot, as opposed
to a medical professional, because one,
medical professional are not necessarily
available in that area, where they have these issues.
And bots have no judgment.
So they are not being watched or being judged by someone.
So we have mental health chat bot.
But we also work with a lot of our partners
to rule out very vertical specific chat
bots, for example, for insurance companies,
where we can do member engagement.
We are also building chat bots for a very specific disease
verticals in the future.
But what they do is that they are somewhat
generalized enough, that now we could take a model like that
and bring our own data and throw our specific data
and train an improved version of that,
that's somewhat focused in the domain that we have.
Of course, we need a lot of data for it to improve.
But then these models then become really valuable,
where you could engage them, for example, in chat bots, where
you could predict the next set of sequence of things
that a user may ask.
So you can give them better services right away.
Or there is enough contextual memory
of the previous conversation, so you know
how to engage the user further.
MARK MIRCHANDANI: Thanks so much for coming in,
Akshay, and telling us about how doc.ai
is working with the cloud.
AKSHAY SHARMA: Thank you, Mark.
This was really exciting.
I'm very happy to be here and hoping
to see many more talks in the world of health care.
That's important for the general ecosystem,
that Google is doing a great job in.
MARK MIRCHANDANI: Absolutely.
Thanks for watching.
If you enjoyed this conversation with doc.ai,
check out the Stack Chat playlist for more great videos.
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