Atif Kureishy: It’s been over the last three years.

We’ve been internally focused on it, but if you’ve seen the sort of rebranding and refresh of our go-to-market, we’re focused on pervasive data intelligence.

Let me break down those three words.

“Pervasive” in the sense of you need to be able to process all this different types of machine data, log data, structure data, curated data, etc.

and process it where it is – in the cloud, on-prem, in object stores, in relation stores.

Increasingly if you do analytics on samples of data, you don’t really get the full view.

Scale becomes a big issue and Teradata has always been about performance at scale.

The second word, “data,” is our legacy.

Finally, “intelligence” is the appreciation of artificial intelligence, and the way of prediction and better insights and understanding is on that data, at scale, everywhere.

So in a lot of ways, it’s not a dramatic pivot.

We’ve been doing distributed algebra and analytics on Teradata forever – the SQL-based capabilities.

Now you’re talking about linear algebra, discrete math, calculus, differential equations.

You’re applying more sophisticated types of math.

When you talk about deep learning, you’re applying more sophisticated math on that data.

But what everyone struggles with is how you do that at scale.

We’ve got the scaling part figured out.

You need to reach beyond just algebra into geometry, which is what you need – Euclidean geometry in a lot of computer vision problems.

But at the end of the day, it’s just math at scale on data, and so that’s what we’re talking about.

insideBIGDATA: And that’s what NVIDIA brings to the table, yes?.How are you guys working with NVIDIA?.Atif Kureishy: Absolutely.

We’ve been a partner with NVIDIA for about one year, part of the Services Delivery Program (SDP).

If we engage with the customers and help them solve deep learning problems, that’s going to push computation on the GPUs.

So obviously, that’s very harmonious.

Coming up next year, we’re actually putting compute into our Vantage platform.

You’re running workload on the Teradata Vantage platform, and that data and computation will be processed on GPUs for training, and serving the inference side.

Ultimately, you’re solving answers and problems for our customers.

Our 2019 focus is Vantage.

We have all the computation and data, along with Teradata Everywhere, AWS, and Azure.

But let’s forget about all of that.

The idea is if you can deliver this in an “as-a-service” manner which really means in a more consumable way to align a business executive.

We can do it in a much more innovative and creative way using machine and deep learning.

But we’re not going to bring all that complexity.

We’re going to give you a subscription or some straightforward consumption-based method offering dashboards, data pipelines, ML frameworks, data labeling/annotation schemes, and GPU infrastructure.

Every enterprise leader in the business wants all of that sophistication without all the complexity, so that’s increasingly what we’re focused on.

insideBIGDATA: What’s the timeframe for these solutions?.Atif Kureishy: It’s an evolution.

You’ll see this carrying along a multi-year strategy.

A lot of folks are doing this in the cloud, so we embrace those partners where it makes sense.

But the Fortune 100, what we call “megadata” customers because of data gravity, privacy, security, etc.

You have to allow them to get to the cloud and that’s a part of our Teradata Everywhere strategy.

You also have to allow them to do analytics at scale in that same Teradata Everywhere environment.

By the way, deep learning is just an evolution of ML.

ML is just an evolution of some of the other modeling and simulation techniques that we’ve been using.

So you have to take customers on that path.

It’s available, or will be available, on AWS and Azure, and on managed cloud.

So those things are available now, so folks can come on board now, and then when the deep learning capabilities come out, they’ll have access to that technology as well.

It’ll be part of a first class environment with Vantage.

The idea is that we’re going to take them on that journey, and be there for them when they need it.

insideBIGDATA: Can you describe a particularly use case?.Atif Kureishy: Yes, there were some creative applications of deep learning at Danske Bank with a variety of transactions involving issuing bank, and receiving bank.

We decided to extend and add new features around everything else we know about the transactions, such as IP addresses, Mac addresses, and other derivative information.

Then we observed that these transactions occur over time, so we were actually looking at sequences of transactions rather than individual transactions.

A lot of machine learning approaches today look at a transaction in isolation in order to do comparative analysis and anomaly detection.

But we were actually looking at sequences of transactions so there’s better signal in that detection.

So we took the sequences arranged over time and we turn that into a model to emulate pixels on an image.

We literally took those transactional features and then did some spatial correlations model techniques and we turned it into image.

Then we applied convolutional neural networks (CCNs) to the image and that became a best-performing method.

We did time-aware LSTMs and other types of recurrent neural networks (RNNs).

The derivative benefit of this approach was that the auditors and regulators could actually see fraud visually.

We showed this kind of pixelization where the intensity of a pixel would actually demonstrate fraud.

They got it, and then applied some other techniques to recognize attributes that contribute to the classifier of false deny or approve.

This was enough for us to understand what these black box models are doing.

In the end, this solution was an ensemble of six different techniques.

We had some logistic regression approaches, some boosted trees, and some other GLMs.

Then we used a deep neural network.

It was such a dramatic improvement.

We worked with them to build their data science capabilities so that they could support this in the future, and that’s why it was such a transformational effort.

insideBIGDATA: Well, this has been great.

I appreciate the opportunity to get a Teradata update.

Atif Kureishy: My pleasure.

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