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Matteo Interlandi on Project Hummingbird
Episode 927th September 2021 • Data Driven • Data Driven
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Hello and Welcome to Data Driven.

In this episode, Frank and Andy speak with researcher Matteo Interlandi about project Hummingbird.  

Audio file 

matteo-mixdown.mp3 

Transcript 

00:00:00 BAILey 

Hello and welcome to dated driven. 

00:00:02 BAILey 

In this episode, Frank and Andy speak with researcher Matteo Interlandi about project Hummingbird. 

00:00:09 BAILey 

Now on with the show. 

00:00:10 Frank 

Second, hello and welcome to data driven. 

00:00:21 Frank 

The podcast where we explore the emerging fields of data science, machine learning and artificial intelligence. 

00:00:27 Frank 

If you'd like to think of data as the new oil, then you can consider us. 

00:00:30 Frank 

Car Talk because we focus on where the rubber meets the virtual road and with me on this epic Rd. 

00:00:36 Frank 

We're on the information superhighway as oh is Andy Leonard. 

00:00:39 Frank 

How you doing Andy? 

00:00:40 Andy 

I'm well Frank, how are? 

00:00:41 Frank 

You I'm doing alright. We're recording this on Wednesday, September 1st, 2021 and the the. 

00:00:51 Frank 

The the remnants of Hurricane Ida are ripping through the DC area. 

00:00:57 Frank 

Uh, so if, uh, if I suddenly get dropped, that's because we probably lost power. 

00:01:03 Frank 

But I do have the backup generator, the one that the professionals installed and my. 

00:01:10 Frank 

Duct taped together a solar generator so. 

00:01:15 Frank 

I will be offline. 

00:01:17 Frank 

For a short. 

00:01:18 Frank 

Bit and hopefully come back online. 

00:01:20 Frank 

How how you doing, Eddie. 

00:01:23 Andy 

I'm doing alright Frank. Well, we are you know I'm about gosh 250 miles South of UM we didn't get near the near the effects of Hurricane Ida as you did. 

00:01:34 Andy 

We're getting a little bit of rain now. 

00:01:36 Andy 

We've had some wind. 

00:01:37 Andy 

Gusts, but it's been really mild, and if you look on the radar. 

00:01:41 Andy 

Gotta watch it into track and I I do. 

00:01:43 Andy 

I'm a weather weenie and amateur but it it just kind of went around us to the to the West and it actually started the east when it got a little north of us and aimed right for your house. 

00:01:54 Andy 

I was looking outside that's where Frank lived, right? 

00:01:56 Andy 

And look, the eye is coming right for. 

00:01:58 Andy 

Frank what's left? 

00:02:00 Frank 

Well, fortunately we're safe. 

00:02:02 Frank 

There was some kind of flooding in Rockville and the small overnight, and some folks they got up. 

00:02:09 Frank 

No one, nobody died that I'm. 

00:02:10 Frank 

Aware of so. 

00:02:11 

It it says. 

00:02:12 Frank 

You know we're not. 

00:02:13 Frank 

Custom the floods or hurricanes or tornadoes up here in DC and and we're more used to the human threats of, you know, little things like terrorism and things. 

00:02:25 Frank 

Like that, but. 

00:02:26 Andy 

Yeah yeah, you guys got a little bit more to worry about that than we do here in FarmVille, right? 

00:02:32 Andy 

But you know these days. 

00:02:33 Andy 

Who knows? 

00:02:35 Andy 

The, uh, definitely our thoughts and prayers are with the folks in in Louisiana and Mississippi. 

00:02:40 Andy 

They were hit very hard. 

00:02:42 Andy 

I've got got friends in Georgia, Western Georgia were telling me that. 

00:02:47 Andy 

They they took a beating as well and you know it just it looks horrible I. 

00:02:53 Andy 

I you know, I've I've been in a few of those places after hurricanes have hit as part of like church efforts to help clean up and stabilize and stuff like that. 

00:03:04 Andy 

It looks like I don't know. 

00:03:06 Andy 

They people describe it as like a war. 

00:03:09 Andy 

I've never been in a war so I don't know. 

00:03:10 Andy 

I've seen pictures and. 

00:03:13 Andy 

There's a lot. 

00:03:14 Andy 

It looks like a lot of stuff is blowing over, and that sort of. 

00:03:16 Andy 

Stuff, it's just. 

00:03:18 Andy 

So, and they're talking weeks and weeks before power comes back on. 

00:03:22 Frank 

That's horrible, that's. 

00:03:23 Andy 

Similar places, yeah. 

00:03:25 Frank 

That's that's. 

00:03:26 Frank 

Probably going to be do more damage from for a lot of things. 

00:03:30 Andy 

Were you worried? 

00:03:30 

But on a. 

00:03:30 Frank 

More positive note, uh, a positive note. 

00:03:31 Andy 

Yes, on a positive note. 

00:03:35 Frank 

Uh, we are. 

00:03:37 Frank 

I am super excited to have a special guest and I say super excited because he's from Microsoft. 

00:03:42 Frank 

He's a senior scientist in Jelt at Microsoft, working on scalable machine learning systems. 

00:03:50 Frank 

Before he was at Microsoft, he was a postdoc scholar at the Computer Science department at UCLA, and this he was doing a lot of interesting stuff there. 

00:04:03 Frank 

He was doing research at Qatar or Qatar. 

00:04:05 Frank 

I'm not sure how to say that exactly, but he has a PhD in computer science. 

00:04:11 Frank 

In university. 

00:04:12 Frank 

Of Modena and or? 

00:04:15 Frank 

I'm going to botch this. 

00:04:15 Frank 

Reggio Emilia. 

00:04:17 Frank 

Welcome to the show, Mateo. 

00:04:22 Frank 

Awesome, so we are really excited to have you here. 

00:04:25 Frank 

We actually booked you a whole month in advance. 

00:04:27 Frank 

I've been looking forward to this. 

00:04:29 Frank 

Yeah, because you're coming by way of some of the folks at the Mlad conference. 

00:04:35 Frank 

And for those who don't know, I'm a I've mentioned this. 

00:04:37 Frank 

Mlad stands for machine learning and data science summit. 

00:04:40 Frank 

It used to be in person I think now it's entirely virtual for the foreseeable future. 

00:04:45 Frank 

Uh, but that why I attended M lads in 2016 summer of 2016 and it was uh, it was life altering like I don't say that. 

00:04:55 Frank 

Lightly so. 

00:04:56 Frank 

So Microsoft does amazing work in the machine learning and data science space. 

00:05:02 Frank 

Very much cutting edge stuff very much I. 

00:05:06 Frank 

I wouldn't say under the radar, but Microsoft does not do a great job putting its own horn, so we're very excited for you to come on Mateo and talk about this little project that you're working on. 

00:05:17 Frank 

And what is the is it have a code name or what? 

00:05:20 Frank 

What is it called? 

00:05:22 Matteo 

Hummingbird should the code name is actually I'm in. 

00:05:26 Matteo 

Don't have any specific internal names for. 

00:05:28 Matteo 

This for this. 

00:05:28 Frank 

OK, what what is GL stand for? 

00:05:32 Frank 

That was my that was my first question. 

00:05:33 Frank 

When I saw your bio. 

00:05:35 Matteo 

Uh is for Gray system lamp and is the after Jim Gray which. 

00:05:41 

Oh, OK. 

00:05:41 Matteo 

Is putting award yeah? 

00:05:45 

OK. 

00:05:46 Matteo 

So these are the search lab after this name yeah and use within the Azure data organization. 

00:05:49 

Oh, interesting. 

00:05:53 Frank 

And uhm, So what? 

00:05:56 Frank 

What what cool stuff does Hummingbird do? 

00:06:00 Matteo 

So, Hummingbird, uh? 

00:06:03 Matteo 

Is a little bit, uh, weird project in the sense that when we started this project we didn't know if it was going to. 

00:06:10 Matteo 

To be a success or not? 

00:06:12 Matteo 

Because what we try to do basically is to uhm translate traditional machine learning models and into neural networks. 

00:06:22 Matteo 

Actually not Internet format into tensor programs such that then we can run over tensor runtime, such as pipers. 

00:06:30 Matteo 

In terms of. 

00:06:32 Matteo 

Uhm, so when we started this project actually idea was hey there is a lot of investment in general pulling into this neural network frameworks and. 

00:06:45 Matteo 

Coming from the Azure data organization, instead, we are more interested in these traditional machine learning methods such as decision trees. 

00:06:52 Matteo 

Linear models were not encoding all those boring traditional algorithms. 

00:07:00 Matteo 

And so we look at this. 

00:07:01 Matteo 

The neural network system and say hey how we can take advantage of all this technology that is built. 

00:07:05 Matteo 

Into this domain so you can run neural. 

00:07:08 Matteo 

Network over CPU. 

00:07:10 Matteo 

Over the GPU, then you can use like fancy compilers to compile to generate the transfer programs. 

00:07:16 Matteo 

All those sort of techniques and we were. 

00:07:19 Matteo 

Kind of struggling. 

00:07:20 Matteo 

To see what we could do with the with this stack and and what we come up with with is this Amber project. 

00:07:27 Matteo 

So we basically take a. 

00:07:32 Matteo 

Traditional machine learning pipelines composed right feature iser and machine learning models. 

00:07:37 Matteo 

After the day trained. 

00:07:39 Matteo 

So first you need to train it using cycle ornamental net or. 

00:07:43 Matteo 

Uhm, uhm, one of those traditional machine learning platforms and then once it is trained we basically convert it into a set of tensor operations in. 

00:07:54 Matteo 

In the current version we use basically PY torch for doing this conversion and then basically you have a pipeline model so you can do whatever you can do with Python. 

00:08:03 Matteo 

Models so you can deploy it in in it into a PY torch. 

00:08:08 Matteo 

Uhm, deployments you can run over CPU ran over the GPU or you can do the torch script if you want to get rid of all the Python dependency and just have a C++ program you can. 

00:08:19 Matteo 

Do all those all those tricks. 

00:08:22 Frank 

Interesting, does it impact accuracy precision? 

00:08:26 Frank 

Does it improve it? 

00:08:27 Frank 

Keep it the same. 

00:08:29 Matteo 

We tried to keep it the same so we are able to keep. 

00:08:33 Matteo 

It The same up to floating point numbers roundings? 

00:08:36 Matteo 

So since we use, you know we use PY torch to run these programs and not like a socket or ornamental net. 

00:08:44 Matteo 

There are some differences in how they do you know, floating point operations. 

00:08:48 Matteo 

So the. 

00:08:49 Matteo 

Accuracy is up to roundings in the Floating Points, which sometimes are actually. 

00:08:54 Matteo 

It can be quite a bit, but most of the time is really small, almost not noticeable. 

00:09:00 Frank 

Interesting, interesting, uhm. 

00:09:03 Frank 

Do you would you know. 

00:09:05 Frank 

If there was like. 

00:09:06 Frank 

A discrepancy, or you Dutch as part of testing? 

00:09:09 Matteo 

It's part of testing. 

00:09:10 Frank 

Right, all software is tested, right Andy? 

00:09:11 Matteo 

So we have we have. 

00:09:13 Frank 

Sometimes intentionally is that the email. 

00:09:15 Andy 

That's right. 

00:09:17 Frank 

And he has a saying where all softwares I I forget exactly what it is. 

00:09:21 Frank 

But what is it? 

00:09:23 Andy 

Yeah, all software is tested, some intentionally. 

00:09:27 Frank 

There you go. 

00:09:30 Frank 

Uhm, so what's the? 

00:09:33 Frank 

What's the real? 

00:09:34 Frank 

What are? 

00:09:34 Frank 

What are the advantages of of of converting kind of a traditional model over to a tensor model? 

00:09:41 Frank 

Is it? 

00:09:41 Frank 

Is it portability? 

00:09:42 Frank 

Is it speed? 

00:09:43 Frank 

You did mention that you can run it on. 

00:09:45 Frank 

You could take advantage of GPU as well as CPU. 

00:09:51 Matteo 

Yes, exactly so you most mostly is related to speed, so you can basically run your socket, learn model on GPU end to end and and this user provides you know a little bit of quite a bit of speed up we for some of our example we even saw like 2 ordinal Magneto speedups. 

00:10:11 Matteo 

For some of the models. 

00:10:13 Matteo 

And uhm, and usually we try to show that. 

00:10:18 Matteo 

If you use GPU. 

00:10:19 Matteo 

Can be much faster, but on CPU we try to be kind of as close as possible scikit learn or the base or the base or diminished model. 

00:10:27 Matteo 

Sometimes we can, sometimes we are a little bit slower. 

00:10:31 Matteo 

Uh, but we. 

00:10:32 Matteo 

We had some really interesting result. 

00:10:34 Matteo 

Like for instance, we did some experiment with some. 

00:10:39 Matteo 

Some folks at the VM and we took some extra boost model and we compiled some training accuracy boost model. 

00:10:47 Matteo 

Uh, using Hummingbird anti VM into some uh, we basically do code generation and we show that the that model that was compiled to Python was even faster than they quoted the C++ implementation that they're having next used, but those CPU and GPU. Yeah, there was kind of OK. What's going on? 

00:11:06 Matteo 

This is not. 

00:11:08 Matteo 

This was not expected. 

00:11:08 Frank 

Wait, did you say it was faster than a C++ implementation? 

00:11:11 Matteo 

Yes, I mean if she used. 

00:11:13 Matteo 

Underneath C++ even scikit learn. 

00:11:15 Matteo 

You know they use like. 

00:11:16 Matteo 

From C++ library and yeah, using TVM for doing the code generation, they are able to do like a operator fusion which you don't normally have for like these traditional models. 

00:11:28 Matteo 

So we told these tricks bigger, basically that are coming from the neural network. 

00:11:31 Matteo 

Famous we were able to get like this. 

00:11:34 Matteo 

These surprising numbers. 

00:11:36 Frank 

Interesting, so that's a real performance boost, and probably if you scale that up into the cloud that probably. 

00:11:44 Frank 

Means a lot of money saving too in terms of on cloud computing things like, I imagine a company like the size of Microsoft would be very interested in getting better results faster with less cloud compute. 

00:11:56 Frank 

You did mention an acronym, I just wanna make sure folks know. 

00:11:59 Frank 

What that is? 

00:12:00 Frank 

Tyvm what is that? 

00:12:03 Matteo 

Uh, I don't know what is exactly for, uh, some tensor maybe? 

00:12:08 Frank 

Andy looks like he knows, but he's on mute. 

00:12:10 Andy 

I don't, yeah I I don't know. 

00:12:13 Frank 

OK, I'm just curious. 

00:12:13 Andy 

I'll go look it up. 

00:12:15 Frank 

There you go. 

00:12:16 Andy 

EVM acronym. 

00:12:19 Matteo 

I think is for tensor virtual machine, but I'm. 

00:12:21 Matteo 

Not sure if this is approach. 

00:12:22 Frank 

That sounds about right. 

00:12:23 Frank 

Tector, yeah tencer. 

00:12:26 Frank 

Vector machine. 

00:12:28 Andy 

Ah, I see. 

00:12:30 Andy 

So thanks very much comes up, that's interesting. 

00:12:34 Frank 

Well, we'll we'll figure out what it is putting. 

00:12:36 Andy 

Put tensor in here at TTVM you said. 

00:12:36 Frank 

This junction so. 

00:12:40 Frank 

Yeah, yeah. 

00:12:40 Matteo 

Yes, is a project is a GitHub project, but I think it also is Apache project and these are our top where you have. 

00:12:45 Andy 

Yeah there TV m.apache.org yeah. 

00:12:50 Andy 

And it doesn't tell me what it stands for, but that's that's where you can go and learn more about it. 

00:12:55 Andy 

It's according to the website and end to end machine learning compiler framework for CPU, GPU's and accelerators. 

00:13:05 Andy 

Interesting, it does sound interesting, yeah. 

00:13:09 Frank 

That's what's great about this space. 

00:13:10 Frank 

There's so much you could geek out on and spend like. 

00:13:15 Frank 

Like I'm just looking through, I found some, uh, a web, an article on machine learning, knowledge dot AI about Hummingbird and it's just like wow. 

00:13:25 Frank 

They basically it looks like they copied and pasted the fake. 

00:13:29 Frank 

From here. 

00:13:29 Frank 

It's intelligent, but it does look fascinating in terms of what it can. 

00:13:35 Frank 

Do so so. 

00:13:36 Frank 

What what motivated what motivated the creation of Hummingbird? 

00:13:43 Matteo 

So the motivation was actually different, so the so the initial motivation was actually tried to. 

00:13:51 Matteo 

To do. 

00:13:54 Matteo 

Uh, not to accelerate. 

00:13:56 Matteo 

The trischen machining pipelines, but to use differentiation. 

00:14:00 Matteo 

Uhm, basically all this, uh, backpropagation. 

00:14:04 Matteo 

All these tools that are using for training over neuron actors and try to translate them over traditional machine learning models. 

00:14:11 Matteo 

So try to do basically backpropagation over scikit learn pipelines. 

00:14:15 Matteo 

And that is the biggest tool. 

00:14:17 Matteo 

So we started with this tool that basically was translating this tradition machine pipelines. 

00:14:22 Matteo 

This second only pipelines at the beginning are into Pytorch such that we can do end to end differentiation. 

00:14:27 Matteo 

But then once. 

00:14:28 Matteo 

We have we were at. 

00:14:29 Matteo 

Point and of course, as you can imagine, we were trying to do end to end differentiation for increasing increasing accuracy of the pipeline to see whether if you use backpropagation you can increase accuracy. 

00:14:40 Matteo 

And then once we did this translation, we basically realized that OK, since we are on Python sword, we can exploit all these other, uh, you know the Python framework and hardware acceleration on those other two rings. 

00:14:52 Matteo 

And then basically we kind of ditch this idea of doing end to end differentiation and running by propagation over over the pipelines and instead we focus more. 

00:15:00 Matteo 

Going to be linear system for accelerating inference prediction over distillation, machine learning. 

00:15:07 Andy 

So I'm curious, Mateo. 

00:15:09 Andy 

This is not my fortune Franks, the data scientists of our pair. 

00:15:13 Andy 

Here I am a data engineer, so can you give me an example of a problem that I I get the speed part of this, I really do. 

00:15:25 Andy 

I we need that in data engineering too. 

00:15:27 Andy 

I think everyone needs needs that performance part, but can you give me an example of something that you've applied this to? 

00:15:34 Andy 

And you already gave us a, you know, a interesting number about how much faster it was. 

00:15:39 Andy 

A couple of good references from that. 

00:15:41 Andy 

Was there something in particular that you've worked on or that your team has worked on and applied this and saw some you know some interesting results? 

00:15:52 Matteo 

So I mean first of all, I'm a database person too. 

00:15:54 Matteo 

I'm not a machine learning, so another I think would be speaking the same language. 

00:15:57 Andy 

OK. 

00:15:59 Matteo 

I'm a I'm a database person that. 

00:16:02 Matteo 

Yeah, it's. 

00:16:03 Matteo 

I'm trying to basically understand all the machine learning domain and see how much that amazing can take advantage of these techniques. 

00:16:10 Matteo 

And my needs help. 

00:16:12 Matteo 

Uh, I mean the the start of my investigation was traditional method because those are the ones that. 

00:16:17 Matteo 

You in general. 

00:16:18 Matteo 

Use or tabular data, that is the one that we have. 

00:16:23 Matteo 

At the most. 

00:16:23 Matteo 

Dumb and so related to use cases. 

00:16:30 Matteo 

Let me think so we. 

00:16:32 Matteo 

Uhm, so we try to use it internally for some of our first party customer. 

00:16:38 Matteo 

Uhm, to just because they have like cyclotron models. 

00:16:42 Matteo 

And they want to kind. 

00:16:43 Matteo 

Of try to see if they can speed up the the inference of this. 

00:16:46 Matteo 

The prediction over these models. 

00:16:48 Matteo 

Uhm, when someone reaching out from outside, uh, mostly with kind of try to accelerate like a 33 based algorithm such as gradient boosting light GBM, extra boost those those. 

00:17:03 Matteo 

Teams and yeah. 

00:17:06 Matteo 

Yeah, in general the use case are really. 

00:17:08 Matteo 

Simple is you know you have a secretary models and you want to deploy your your your secretary models. 

00:17:14 Matteo 

Uh, and when you deploy you want to take advantage of GPU. 

00:17:18 Matteo 

You did because you already have some GPU deployments, so you already have some neural network. 

00:17:22 Matteo 

Uh, there and uh you also want to take advantage of the GPU that you are in your deployment by with this. 

00:17:30 Matteo 

Yeah, traditional models or just because you have like a a traditional model, you want to increase the the inference time. 

00:17:32 

Got you? 

00:17:38 Matteo 

I have to say that the most of the performance boost we usually see is related to batch inference, so not when you're doing one single one single point inference, but when you have like a batch of records that we can basically saturate the performance of a GPU of a GPU order for instance. 

00:17:55 Andy 

So just to follow up on that, then it sounds like a lot of what you're doing is. 

00:18:02 Andy 

You know you're focused on the on the tool that does these translations for you into other platforms. 

00:18:08 Andy 

Other technologies allows you to use you know GPU versus CPU, and I think what you're creating if I understand you and I didn't do my homework, apologies. 

00:18:20 Andy 

I think what you're building is away. 

00:18:22 Andy 

To to to exactly what we were joking about earlier about testing. 

00:18:27 Andy 

You want to see how can I get the peak performance? 

00:18:31 Andy 

For you know this part of of that. 

00:18:33 Andy 

Maybe this module or this operation of the batch and maybe the answer here and you mentioned this may be the answer here. 

00:18:41 Andy 

Is CPUs or GPUs? Maybe it's C++ and you're just able to, you know, kind of pick the high spots and say I'm getting order. 

00:18:50 Andy 

Case of performance. 

00:18:51 Andy 

The low spots right? 

00:18:52 Andy 

Just stuff that runs it fast. 

00:18:54 Andy 

And then you can put that together and hand it back to your client or someone who's interested in it and say right now, given the volume and the data and the state of hardware, you can get the maximum performance. 

00:19:07 Andy 

If you do this part here and that part there, that part there is that fair. 

00:19:13 Matteo 

So you're you're actually looking into the some future work that we are investigating now so kind. 

00:19:18 Matteo 

Of is matching. 

00:19:18 

OK. 

00:19:19 Matteo 

The different for the different part of the pipeline. 

00:19:22 Matteo 

So what we focus actually right now is try to translate the machine learning models end to end, so taking the featurization's and all the models and. 

00:19:31 Matteo 

Then because basically we saw that that is the the where we can get most of the time, that is where we can get to the mass, the mass maximum performance because by looking at the model end to end we can run it completely over the GPU instead of having to go back and forth from GPU to CPU for example. 

00:19:47 Matteo 

But what you point out is something that we are considering. 

00:19:51 Matteo 

So kind of look at the model, not as a kind of, you know, a unique. 

00:19:55 Matteo 

The black box kind of a artifact, but is something that we can actually split in different parts and eventually we can run it in over different over different hardware over different runtime. 

00:20:08 Matteo 

I'm such such TV. 

00:20:09 Matteo 

As I said before, so some particle on TV and some parts random Pytorch the the sort of those sort. 

00:20:14 Frank 

Of things so kind of like a meta optimizer. 

00:20:15 Andy 

OK, it's a combination. 

00:20:18 Andy 

Like that's exactly where I was going. 

00:20:19 Andy 

Yeah, it's like you're tuning stored Procs Mateo. 

00:20:24 Andy 

And you're deciding I want this one to run on SQL Server. 

00:20:27 Andy 

I want that one to go to Postgres. 

00:20:29 Andy 

And yeah, it's just that that is interesting that you can span hardware and software. 

00:20:36 Andy 

You can pick platforms in the software. 

00:20:39 Andy 

To do it. 

00:20:40 Andy 

And I I'm with you. 

00:20:41 Andy 

I got my head around us now and I I think that's really really cool I the this just sounds like something that's going to accelerate the field really. 

00:20:51 Andy 

Because if you the last time you're sitting around twiddling your thumbs waiting for a result, you know the more you can get done. 

00:20:59 Andy 

I mean, that's just. 

00:21:00 Andy 

Common sense, so I love what you guys are doing. 

00:21:01 

Yeah, yeah exactly. 

00:21:04 Andy 

That's that's really cool and I like that. 

00:21:07 Andy 

I don't think I've ever heard anybody talk about. 

00:21:10 Andy 

You know, changing libraries and changing you know hardware platforms even. 

00:21:17 Andy 

I mean it's it's hard to even say I don't know what you'd even classify that as because running different chips you know, running the processes on different chipsets. 

00:21:26 Andy 

That's something we used to do back. 

00:21:28 Andy 

In the seventh, you know. 

00:21:29 Andy 

I mean, but it was. 

00:21:30 Frank 

Let's just say that Harkins back to like the. 

00:21:31 Andy 

Mainframe days it kind of does. I mean 68 hundreds and his the 80s and all of that and but? 

00:21:39 Andy 

I mean, this is way, way, way more advanced than all that, but I like the idea. 

00:21:46 Andy 

I like being able to to do that and I hear what you're saying right now. 

00:21:50 Andy 

You're just after picking a platform, picking on an approach and saying, you know we're going to run this. 

00:21:57 Andy 

We're going to generate C++. It's going to run on CPU's, and that's overall that's going to be your fastest result. It's going to give you your best performance. 

00:22:06 Andy 

I I get you. 

00:22:07 Andy 

But that I I didn't realize I jumped ahead there. 

00:22:10 Andy 

But that happens sometimes rare, but it happens. 

00:22:15 Andy 

Y'all could totally take that idea Mateo and run with. 

00:22:18 

Yeah, if you. 

00:22:19 Matteo 

You can run right the paper together if you want to. 

00:22:22 Frank 

There you go. 

00:22:22 Frank 

You know, right? 

00:22:23 Andy 

Away I could. 

00:22:24 Andy 

I could do the punctuation. 

00:22:28 Frank 

He's really good at. 

00:22:29 Frank 

Reviewing stuff, I will say that his personal experience from him him reviewing my articles in the now defunct MSDN magazine. 

00:22:31 

Here we go. 

00:22:38 Andy 

I remember that those were fun. 

00:22:39 Andy 

I learned a lot reviewing your articles. 

00:22:42 Andy 

Frank 'cause you were always on the cutting edge. 

00:22:44 Frank 

I try. 

00:22:45 Andy 

Yeah, neat stuff what? 

00:22:46 Frank 

But this this Hummingbird stuff looks really cool and it looks like it's as easy to install as PIP install Hummingbird. 

00:22:54 Matteo 

Just be missing. 

00:22:54 Frank 

Hummingbird, Dash MLI think it is. 

00:22:57 Matteo 

Yes, yeah, that number was already taken off course. 

00:23:00 Frank 

Well, yeah, but no. 

00:23:02 Frank 

This is really cool. 

00:23:02 Frank 

Like I I I like where this is going. 

00:23:05 Frank 

I like the potential for it. 

00:23:06 Frank 

'cause you with the cloud you know you. 

00:23:09 Frank 

You think about. 

00:23:11 Frank 

Database as a. 

00:23:12 Frank 

Service like you don't. 

00:23:13 Frank 

You know you don't care what the heart women you care but I mean like from the end developers point of view. 

00:23:19 Frank 

They won't necessarily care what type of hardware like that. 

00:23:21 Frank 

This does open. 

00:23:22 Frank 

Up some very interesting possibilities, just just kind of piggybacking on kind of what Andy said. 

00:23:27 Frank 

It's like, wow, I mean one of the things and I forget who said it? 

00:23:31 Frank 

Might have been Kevin Hazzard, who said that you know now we live in an age where we're not dealing with just spinning platters. 

00:23:39 Frank 

We can imagine. 

00:23:41 Frank 

What database time butchering what he said? 

00:23:44 Frank 

But he he did say he says a lot of profound things and one of the most profound things he said was something like you know what? 

00:23:50 Frank 

What would a database in the future look like? 

00:23:52 Frank 

Because we're not. 

00:23:52 Frank 

Dealing with spinning platters is that did. 

00:23:54 Frank 

I get that right Andy or something along those lines. 

00:23:55 Andy 

You did he. He blogged about it out devattorney.com. We'll have to look that up with the show news, but Kevin is one of those. 

00:24:06 Andy 

He's a pretty pretty, profound thinker, and 

00:24:08 Frank 

I was going to say, uh, she's a very deep thinker like he's always like 10 moves ahead. 

00:24:09 Andy 

Yeah, I could tell. 

00:24:14 Andy 

Yeah, and I could tell reading the article 'cause I've known him for it. 

00:24:18 Andy 

Sort of you. 

00:24:19 Andy 

We've known him for a decade or more and he was struggling with trying to articulate the concept. 

00:24:25 Andy 

And if it's tripping someone like Kevin Hazzard up, it's pretty powerful console. 

00:24:30 Frank 

Right, right? 

00:24:31 Andy 

But he did a good job in devjourney.com. He's not blogging as much 'cause he's just too stinking busy. But yeah, you're right. It. And I had a similar conversation. 

00:24:44 Andy 

With you know with with my son Stevie Ray not too long ago we were talking about. 

00:24:52 Andy 

You know flash drives, and you know that the memory that we have now is so much faster than the platters and I I made this comment to him and I kind of stopped and thought I don't know if that's accurate or not and maybe Mateo since you're here working on a cutting edge, you can help us. 

00:25:08 Andy 

We were just poking around thinking about operating systems. 

00:25:11 Andy 

And we do a lot are here at the House in FarmVille, VA with IoT. 

00:25:16 Andy 

In fact, he's building a new collection of sensors for me right now for nor do we know. 

00:25:20 Andy 

So we're going to hook it to a π, because Pi's can talk to, you know, to the Internet they can talk to our router, and that's the next big secret. Don't tell anybody. 

00:25:31 Andy 

Kidding, but. 

00:25:33 Andy 

It's the one of the neat things about these Pi architectures versus even really powerful service that we have right now is both. 

00:25:42 Andy 

You can compare them. 

00:25:43 Andy 

They're both messaging systems, they're they're just passing around messages physically on a bus. 

00:25:47 Andy 

When you get to that Pi level, and that's how I learned it, so I'm really excited about him learning. 

00:25:52 Andy 

That way, but. 

00:25:53 Andy 

Nobody thought about because we didn't. 

00:25:55 Andy 

We couldn't conceive of it when hard drives came out. 

00:25:58 Andy 

Nobody thought about building. 

00:26:00 Andy 

The OS or something. 

00:26:02 Andy 

Second, you know second generation or higher language on that without those spinning disk. 

00:26:08 Andy 

And here's the here's my long winded place. 

00:26:11 Andy 

I wanted to get to is I don't know. 

00:26:15 Andy 

If we're there now, even I imagine there's probably some OS is out there that. 

00:26:22 Andy 

Or setting on GitHub, there's probably 100 of them by now that people are exactly doing that. They're taking advantage of the new IO if you will, but I don't think the big systems are doing it. I don't think the major popular operating systems are and for good reason. They're stable, it's. 

00:26:42 Andy 

It's hard to change all of that. 

00:26:42 Frank 

Well, there's a lot of inertia. 

00:26:45 Frank 

When you when you have a widely deployed operating system, you you get a lot of inertia and you know I'm not. 

00:26:51 Frank 

And I'm not talking about just Windows, I mean iOS. 

00:26:53 Frank 

I mean Android, I mean Linux like. 

00:26:54 

Sure, sure. 

00:26:55 Frank 

Once you have a wide install base, you you lose the. 

00:26:58 Frank 

Ability to be very experimental. 

00:27:01 Andy 

Yeah, I totally concur with them and I see. 

00:27:05 Andy 

I see the cloud, I see Azure. 

00:27:07 Andy 

I see the you know that this leap that's happened and it's just it's crazy to try. 

00:27:13 Andy 

I don't even keep up with it, but just reading tidbits, reading, editing Franks articles and the like, it's just taking these quantum leaps. 

00:27:21 Andy 

It's like 10 years worth of stuff happening every six months. 

00:27:26 Andy 

And you guys just keep knocking it out, and I imagine at some you know at the Gray Systems lab that you're surrounded by people who are just, you know, in Star Trek land or something. 

00:27:41 Matteo 

Happy yeah yeah. 

00:27:44 Matteo 

Yeah I totally agree on every. 

00:27:45 Matteo 

All the things that you said. 

00:27:46 Matteo 

Like I I was presenting a project related to Hummingbird. 

00:27:50 Matteo 

Actually kind of like a few days ago and I was preparing my. 

00:27:54 Matteo 

And I and I. 

00:27:55 Matteo 

Come up with this slide, I think. 

00:27:56 Matteo 

It was from just. 

00:27:57 Matteo 

Doing a few years back and. 

00:27:59 Matteo 

It basically was showing the number. 

00:28:01 Matteo 

Of papers that. 

00:28:01 Matteo 

Were published on machine learning or the public on archive and in in 2018 they were published 100 paper a day just to machine learning on that kind of just. 

00:28:11 Andy 

My fingers. 

00:28:13 Matteo 

Just to give an idea on how fast is now, the pace in which innovation is coming up, especially when the machine learning neural network domain is just. 

00:28:22 Matteo 

On on operating system database domain is a little bit slower, I would say because a Frank said that there is an answer there because this system are deployed and if you want to add even new hardware it will takes it takes forever. 

00:28:37 Matteo 

So I say Microsoft what happens when you have like a new outdoor community and you want to exploit it? 

00:28:42 Matteo 

It just sticks. 

00:28:45 Matteo 

And this is just because you know they're used by many people, and even if you want to do a small change here, sweetheart. 

00:28:53 Andy 

And I'm seeing the articles about Windows 11 where when you try to make a change like that and say hey you need this minimum hardware. 

00:29:00 Andy 

Now everybody is going. 

00:29:03 Frank 

Oh yeah, yeah, everybody got the pitchforks out and like freaking out and like, yeah, I mean I, I remember I was at I was at Microsoft doing evangelism on the shift to Windows 8. 

00:29:15 Frank 

Just you would not believe this. 

00:29:17 Frank 

Well maybe you would, I don't know. 

00:29:18 Frank 

But like just the the horror and people faces when they got rid of the start button like it was just like it was like the end of the world like you were you were killing somebody grandma. 

00:29:26 Frank 

Like you know it's just. 

00:29:27 Frank 

Like it was, just like I mean, I disagree with the decision that was made, but but let's let's put it in perspective. 

00:29:34 Frank 

You know? 

00:29:37 Frank 

But, uh, but yeah, I mean. 

00:29:37 Andy 

You could still get there. 

00:29:40 

You can still start. 

00:29:41 Andy 

Things, but you could. 

00:29:42 Frank 

Still start things like in and and before. 

00:29:46 Frank 

This is funny like this is this is just a complete sidetrack in material. 

00:29:50 Frank 

We do this a lot. 

00:29:51 Andy 

'cause it never happens. Mateo. 

00:29:53 Frank 

Before keyboards had the Windows Key, there's a you can hit control escape and it pulls up the same thing like. 

00:30:01 Frank 

Like I don't know like it's just. 

00:30:03 Frank 

Not the end. 

00:30:03 Frank 

Of the world anyway, sorry it flashed back to 2012, but so Mateo. 

00:30:10 Frank 

We have a bunch of kind of pre canned questions we're going to ask you. 

00:30:14 Frank 

We ask this from all of our guests. 

00:30:16 Frank 

Most of them are about half of them, or kind of fill in the blanks, but the first one is how did you find? 

00:30:22 Frank 

Your way into data. 

00:30:23 Frank 

Did you find data or did data find you? 

00:30:27 Matteo 

Uh, I would say data finally. 

00:30:32 Matteo 

I think it was mostly because when I started my PhD, I wanted to do distributed systems. 

00:30:39 Matteo 

And for some reason I end up doing distributed system in a lab in a database lab. 

00:30:44 Matteo 

So I think that is why I think the data found me because I want I wanted to do something else. 

00:30:49 Matteo 

But then I end up doing data that probably was. 

00:30:54 Matteo 

I was really lucky to be honest. 

00:30:57 Andy 

Cool, very cool. 

00:31:00 Andy 

So our second question is what's the favorite part? 

00:31:03 Andy 

Your favorite part of your current job? 

00:31:08 

Uh, no, this is. 

00:31:09 Matteo 

A hard question. 

00:31:11 Matteo 

Uh, I will say that I really love my management in the sense that they allow me us in general to be. 

00:31:20 Matteo 

We sort of independent in the sense that you know we are researcher and they allow us. 

00:31:28 Matteo 

They they find a way to. 

00:31:30 Matteo 

Kind of strike. 

00:31:31 Matteo 

A balance between having us be independent and kind of do our own research with crazy ideas like the one that. 

00:31:37 Matteo 

I presented with Hummingbird. 

00:31:39 Matteo 

And still be kind of, you know. 

00:31:41 Matteo 

With our foot on the ground and and kind of helping product improve improve. 

00:31:46 Matteo 

The system etc. 

00:31:48 Matteo 

So I think that is mostly what I love, so I on one I I can kind of look in what we. 

00:31:53 Matteo 

Can do next. 

00:31:54 Matteo 

Like having the operators running over different target and on the other I can kind of see what are the real problems that are coming from from from product and how we. 

00:32:03 Matteo 

Can solve. 

00:32:03 Matteo 

Them and I love this to be honest and I love this. 

00:32:08 Frank 

Awesome, our first complete this sentence when I'm not working I enjoy blank. 

00:32:15 Matteo 

I would say work but they will not. 

00:32:23 

Yeah, I don't know. 

00:32:25 Matteo 

Maybe family at this point, maybe family spending a lot of time in family with the commute time. 

00:32:29 Matteo 

We are often at home and I have a two years old that is driving us nuts. 

00:32:39 Andy 

That's pretty cool. 

00:32:41 Andy 

So we have. 

00:32:41 Frank 

My youngest did zoom kindergarten over zoom and it's just as chaotic as it sounds. 

00:32:47 Frank 

Almost put it that way. 

00:32:50 Matteo 

Yeah, I cannot imagine I mean to be honest. 

00:32:52 Matteo 

Now he's in daycare and we are really happy that now is in daycare because I'm, you know, at that age. 

00:32:57 Matteo 

But I guess that every kid needs to have interaction with. 

00:33:00 Matteo 

The with other. 

00:33:01 Matteo 

Kids and just stay at home is not, is not is not healthy, but I can't imagine how. 

00:33:06 Matteo 

Hard it is to. 

00:33:07 Matteo 

Have like one year at home and. 

00:33:09 Matteo 

Having class or two courses. 

00:33:12 Matteo 

Yeah, I agree. 

00:33:15 Andy 

Go ahead, I'm sorry. 

00:33:17 Matteo 

Joe said, I hope that this all. 

00:33:18 Matteo 

This situation will end soon. 

00:33:20 Frank 

Me too yeah. 

00:33:21 Matteo 

It means it doesn't like you, but. 

00:33:23 Andy 

Yeah, same here. 

00:33:25 Andy 

I think we all do the uh, I think it's going to be one of those things where we look back for decades probably, and see these little things that we're really not noticing right now. 

00:33:36 Andy 

We're just coping and managing and going on that. 

00:33:40 Andy 

You know, we're gonna look back and go. 

00:33:41 Andy 

Wow, you know that changed this. 

00:33:44 Andy 

And that, and there's all these things that come from it. 

00:33:47 Andy 

I, I hope, mostly good. 

00:33:48 Andy 

But I think it takes us time to figure out the good. 

00:33:53 Andy 

I I look forward to that time. 

00:33:56 Andy 

When we are. 

00:33:56 Andy 

Reflecting and reminiscing on stuff like this. 

00:34:01 Andy 

I I want to, but we have to be on. 

00:34:03 Andy 

The other side though. 

00:34:05 Andy 

Yes, our our second of three complete descendants is is, I think, the coolest thing in technology today is blink. 

00:34:20 

I I. 

00:34:23 Matteo 

I mean, there's other. 

00:34:24 Matteo 

Search, usually I'm attracted by things that I don't know. 

00:34:28 Matteo 

Uh, so we'll say something like quantum computing because I don't know anything about quantum computing. 

00:34:36 Matteo 

Yeah, I I don't know. 

00:34:39 Frank 

So go to impactquantum.com. 

00:34:44 Andy 

I'm smiling because I was waiting for Frank. 

00:34:46 Frank 

I actually it's funny because in the I. 

00:34:50 Frank 

Went to the last M lads that was held in person. It was fall 2019 and the second day keynote was a hardware keynote and you know I go to uh, data science conference. 

00:35:01 Frank 

I want our data science like I I was kind of mad that they had a hardware person up and but then she started talking about quantum and it was just blew. 

00:35:08 Frank 

My mind, and ever since then I I. 

00:35:11 Frank 

I've really wanted to, I really. 

00:35:14 Frank 

I was just so overly excited about, like quantum computing, but the thing about quantum computing is, you know that night at the hotel. 

00:35:22 Frank 

Like you know I installed the Q Sharp SDK and stuff like that and then I was like OK Now what? 

00:35:27 Frank 

Because it made no flippin sense. 

00:35:32 Frank 

So I've been kind of on this, you know, intermittently, this journey of kind of learning more about quantum computing, so starting the podcast on impact quantum and then starting kind of like the blog. 

00:35:42 Frank 

Have kind of forced me to keep at least the regular cadence of figuring out what's going on there, so it's it's fascinating. 

00:35:49 Frank 

I will say the one thing I've learned is the importance of linear algebra. 

00:35:53 Frank 

Apparently, linear algebra and the way the algorithms work in quantum systems tend to explain each other very well so. 

00:36:02 Frank 

But yeah, so definitely a quad impact. 

00:36:05 Frank 

Quantum.com is. 

00:36:06 Frank 

A blog I've I've started last week and regularly updating it, but that way. 

00:36:13 Frank 

But that's you know, ending the shameless plug. 

00:36:15 Frank 

But I agree with you, I think quantum computing would be a very cool thing to explore for a number of reasons. 

00:36:21 Frank 

The the next and final completed sentence is I look forward to the day when I can use technology to blank. 

00:36:32 Matteo 

He used technology and I cannot have to drive the car that is like censoring cars is something I live in Los Angeles, so for me it's half dozen cars. 

00:36:40 Matteo 

Can be. 

00:36:40 Matteo 

Kind of complete life change. 

00:36:45 Frank 

I totally agree, I I I used to enjoy driving like I used to. 

00:36:50 Frank 

I grew up. 

00:36:52 Frank 

I I didn't have a license that was like 21 so like it was just like for me. I've done my time on mass transit. 

00:36:57 Frank 

I'll put it that way, but like living in DC Everywhere is just bumper to bumper to do. Probably a lot like LA and it just really takes the joy out of it. And you know. 

00:37:10 Frank 

One of the things my last job. 

00:37:11 Frank 

At Microsoft I was at the MTC. 

00:37:13 Frank 

And the only thing I didn't want to take that job was because I had to drive to Virginia. 

00:37:20 Frank 

Which despite it being 9 miles of the crow flies could take. 

00:37:25 Frank 

Could take 90. 

00:37:26 Frank 

Minutes to two hours, but as I don't want to say as luck would have it, 'cause it certainly wasn't lucky. 

00:37:33 Frank 

The pandemic kind of made it so I could work remotely and never had to do it. 

00:37:37 Frank 

But you know, I I I share your dream. 

00:37:40 Frank 

At day of the. 

00:37:41 Frank 

Of the driverless of the you know self driving cars so you can. 

00:37:44 Frank 

You can read you can you know be on the computer you can do work while you're driving and things like. 

00:37:48 Frank 

That yeah, I'm I'm right there with you. 

00:37:51 Matteo 

Yeah, I I totally agree. 

00:37:52 Matteo 

With what you said. 

00:37:53 Matteo 

I mean, I'm from I'm from Italy and now I'm from Montana, which is where. 

00:37:59 Matteo 

Basically, we say we like a fast car and good food, so we have like Ferrari we have Ducati we have. 

00:38:06 Matteo 

They rolled into over that so. 

00:38:08 Matteo 

I was growing up with like hearing the Ferrari when they tried in. 

00:38:11 Matteo 

The in the. 

00:38:13 Matteo 

In the circuit AV in Chirag no. 

00:38:16 Matteo 

I I. 

00:38:16 Matteo 

Leave like I think 3. 

00:38:18 Matteo 

Or 4 miles from Fiona is still like a year when they turned. 

00:38:21 Matteo 

The engine on how? 

00:38:22 Matteo 

Loud were was that so I really like cars but. 

00:38:25 Matteo 

Yeah, I can not stand. 

00:38:28 Matteo 

You know, I believe the traffic line with other cars just for like for instance for going to work or to for going grocery shops. 

00:38:35 Matteo 

And it's just kind of a waste of time. 

00:38:37 Frank 

Especially Ferrari, Ferrari is meant to go run free. 

00:38:42 Andy 

Yes, yes. 

00:38:44 Andy 

But that thing in Texas. 

00:38:46 Frank 

That's right my my neighbor, a couple of my neighbors have. 

00:38:48 Andy 

Let her go. 

00:38:51 Frank 

Of one of my neighbors has a Ferrari and you can hear it go by. It sounds beautiful here go by so I totally relate somebody down the street owns a Jaguar V12. 

00:39:05 Frank 

And when that thing goes by, it's like angels singing I. 

00:39:09 Frank 

I know it's a British car and an Italian car, and that's probably heresy. 

00:39:12 Frank 

But I will say it is sounds sounds impressive. 

00:39:16 Frank 

Uh, so so it sounds like. 

00:39:20 Frank 

You might also be a car guy. 

00:39:22 Frank 

Or at least used. 

00:39:23 Frank 

To be yeah. 

00:39:24 Matteo 

Yeah, yesterday. 

00:39:26 Andy 

Back home 

00:39:28 Andy 

So our next one is share something different about yourself, but a little caution. 

00:39:35 Andy 

It's a. 

00:39:36 Andy 

It's a family friendly podcast. 

00:39:38 Andy 

We want to keep that iTunes clean rating here, so don't make us at it. 

00:39:48 Matteo 

Yeah, I don't know. 

00:39:49 Matteo 

I mean I don't know what about to share really. 

00:39:51 Matteo 

I'm kind of spending all my time either I work with or with family, so I probably have the boring life ever. 

00:39:58 Matteo 

Do you think that? 

00:40:00 Matteo 

I I think it is good. 

00:40:02 Matteo 

I mean I don't know. 

00:40:02 Matteo 

If it's good, the fact that now we are. 

00:40:04 Matteo 

Working from home. 

00:40:05 Matteo 

I have kind of more time to. 

00:40:08 Matteo 

Focus on other different things. 

00:40:10 Matteo 

Like for instance, I could watch stops right before I couldn't watch stocks, and while I was at work. 

00:40:16 Matteo 

Uh, because I can drive my laptop and when I have a meeting I can just take a take. 

00:40:20 Matteo 

A peek and of course I can strip my stock there. 

00:40:23 Matteo 

Uh, while while I'm while I'm working. 

00:40:27 Matteo 

Uh, and yeah, and like I think it kind of yeah, kind of like a uh. 

00:40:33 Matteo 

Kind of looking at the stock market, especially because now is. 

00:40:37 Matteo 

A little bit. 

00:40:37 Matteo 

There's a little bit of fraud around, so all these mem, stock, etc. 

00:40:41 Matteo 

Is you make exciting, but there's a little bit dangerous so. 

00:40:48 Frank 

It's become like a sport and if you will. 

00:40:52 Matteo 

Yeah, I mean I was trying this then. 

00:40:55 Matteo 

Auto renewed app. 

00:40:56 Matteo 

When they say gamification of stock market, I don't know if you haven't tried that is is crazy. 

00:41:00 Matteo 

It looks like gambling at all. 

00:41:03 Frank 

Right? 

00:41:03 Matteo 

It looks like. 

00:41:08 Frank 

And the final question, do you listen to audiobooks, and if so, do you have any recommendations? 

00:41:16 Matteo 

No, I don't listen to any books. 

00:41:18 Matteo 

I think I'm more kind of on the old. 

00:41:20 Matteo 

Style I would say I. 

00:41:23 Matteo 

I prefer using it to read. 

00:41:25 Matteo 

Uh, rather than listen. 

00:41:27 Matteo 

You know, I. 

00:41:28 Matteo 

Don't know why. 

00:41:29 Matteo 

I don't know why. 

00:41:31 Frank 

I think it. 

00:41:31 Frank 

I think it depends on the person like. 

00:41:33 Frank 

I think it depends on kind of what you're comfortable with. 

00:41:36 Frank 

I mean, my audiobook listening is nowhere near where it was when I would drive everywhere all the time. 

00:41:42 Frank 

So yeah, yeah. So the reason we asked him 'cause audible is a sponsor of the show and if you go to the data drivenbook.com you can sign. 

00:41:53 Frank 

Up for free. 

00:41:53 Frank 

Audible membership and if you sign up then they give us a a little pat on the back and probably enough money to buy a Starbucks. 

00:42:02 Frank 

Help support the show. 

00:42:05 Frank 

And they've actually been one of our number one. 

00:42:07 Frank 

Sponsors so far. 

00:42:08 Frank 

Because of this program so. 

00:42:10 Frank 

Yeah, so you mentioned you had a website where can folks find out more about you? 

00:42:19 Matteo 

Who is my my website? 

00:42:20 Matteo 

I think it is. 

00:42:22 Matteo 

I I don't remember. 

00:42:24 Matteo 

Uh oh, into result is a GitHub website into result Dot GitHub dot IO. 

00:42:29 Frank 

All right, we'll make sure it goes on the show. 

00:42:32 Frank 

Notes so folks can find out more about this and definitely go to your favorite command line prompt and type in PIP install Hummingbird Mel to check out what's going on. 

00:42:44 Frank 

I'm definitely going to experiment with this. 

00:42:46 Frank 

'cause it does look fascinating and and like Andy said, the potential for this is fascinating. 

00:42:52 Frank 

Because this could end up in, this could end up in a lot of different places, 'cause it solves a lot of different problems. 

00:43:00 Frank 

So anything else would fail. 

00:43:03 Matteo 

Yeah, if you try it let us know and we are kind of, you know, looking for contributors and feedbacks. 

00:43:08 Matteo 

So if you try it let us know what do you think and how we can improve. 

00:43:12 Frank 

Awesome, thanks and I'll add the nice British lady and the show. 

00:43:16 BAILey 

Thanks for listening to data driven. 

00:43:18 BAILey 

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00:43:20 BAILey 

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00:43:24 BAILey 

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