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LORIN PRICE: Today, we are here with Harshit
who is a senior ML engineer with Quantiphi,
and we are super excited to learn about some
of the machine learning solutions built by Quantiphi.
Before we get started, can you tell
us a bit more about Quantiphi?
HARSHIT MALIK: Sure.
So we are an AI ML company at our core,
and we provide end-to-end data as well as ML solutions
for our customers.
We've been around for over six years,
and we have offices in the United States, Canada,
and India.
We are also a Google Cloud Certified Partner
and we have been awarded the Google ML Partner
of the Year for 2017 and 2018.
LORIN PRICE: Awesome.
Can you talk more about what it means
to be a Google Cloud Partner?
HARSHIT MALIK: Sure.
So we've been a Google Cloud Partner
for about four years now.
And we have over 325 employees who are Google Cloud Certified
Professionals.
So what this partnership means to us
is that we are often at the forefront of newest GCP tools
and technologies available.
We get to be in contact with the product development team
in order to provide solutions to our customers.
And also, we're able to provide feedback
back to the product development team
so that they can better design newer.
Features.
LORIN PRICE: Great.
Tell us about QDox.
HARSHIT MALIK: Sure.
So QDox is an enterprise grade productized document processing
solution.
So it's designed to handle oppressive workloads
of converting documents into usable data.
So it uses a combination of traditional OCR and custom
deep learning models for handling virtually any template
of documents and to convert into a knowledge base which
can be leveraged for any further business uses.
We have a ton of features available in QDox,
so for example, entity recognition, key value bearing,
digitizing unstructured and structured tables,
digitizing charts, graphs, maps, and so on.
And also under the hood, we have an active learning pipeline
which ensures continuous improvement of our ML models.
So any feedback the user provides,
helps us in improving our own ML models, as well.
LORIN PRICE: Can you talk about some
of the specific technologies that were used
when you were building QDox?
HARSHIT MALIK: Absolutely.
In order to keep our design as scalable
and customizable as possible, we used a lot of GCP tools
for building a solution.
From the start to the end, basically, the user
has the option of uploading their documents
to Google Cloud via multiple sources, like you UI
or emails or simple drag and drop.
All of these documents are stored in Google Cloud Storage
as a staging area, which triggers a Cloud Task which
sends out asynchronous triggers to App Engine for processing
these documents.
So we use Vision API for converting this image
and document into text.
And we also have custom ML models,
which are containerized and stored
in App Engine for handling all of the ML tasks.
Now, the results can be stored in various data storages.
So if you want relational database,
you can use, for example, a BigQuery or Cloud SQL,
or if you want a no SQL database,
you can use Bigtable or Firestore.
And if you also want any visualization or business
analytics, you can port these results
to any analytics tool of your choice,
say Looker or Tableau or Data Studio.
LORIN PRICE: What are some of the challenges
that you encountered when building QDox?
HARSHIT MALIK: We designed QDox with an evolutionary
architecture so that it's capable of recognizing
any challenges and adapting to it in the future.
So some of the challenges we faced were firstly,
we had data shortage issue.
So we realized that if we need to provide
high standards of accuracy, we might not
be able to cover all templates.
So user feedback is very important in that scenario.
So we included the active learning pipeline.
So anytime a user provides feedback
for a particular document, that document becomes a data
point which helps in retraining our models
and ensuring that the accuracy level keeps on increasing.
Secondly, another challenge which we faced
was that earlier, we had used Cloud Functions
for deploying our ML models.
What we found out that our ML models were too computationally
heavy, and we would often face a timeout issue
in Cloud Functions.
So instead, we used App Engine, which integrates well
with Cloud Tasks, and it's also able to scale up endlessly
for our ML models, as well.
LORIN PRICE: Great.
Well, Harshit, thank you so much for being here with us today
and for sharing more about QDox.
HARSHIT MALIK: Thank you for having us.
LORIN PRICE: If you'd like to learn more about Quantiphi,
you can visit Quantiphi.com.
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Thanks for watching, and stay tuned for more episodes
of Stack Chat.