Welcome back to backspace Academy. In this lecture we're going to be running 2
through some of the analytics and machine learning services on AWS Amazon 3
Elastic MapReduce or EMR for short is AWS's Hadoop framework as a service 4
you can also run other frameworks in Amazon EMR that integrate 5
with Hadoop such as Apache spark HBase Presto and Flink. Data can be analyzed by 6
EMR in a number of AWS data stores including Amazon s3 and Amazon DynamoDB 7
Amazon Athena allows you to analyze data stored in an Amazon s3 bucket using 8
standard SQL statement. Amazon Elastic search is a fully managed service for 9
elastic.co's elasticsearch framework this allows high-speed querying 10
and analysis of data that is stored on AWS. Amazon Kinesis allows you to collect 11
process and analyze real-time streaming data. Amazon quicksight is a business 12
intelligence reporting tool similar to tableau or if you're a Java programmer 13
similar to BIRT and is fully managed by AWS 14
so let's have a look at some of the machine learning services on AWS 15
AWS deeplens is a deep learning enabled video camera, it has a deep learning 16
software development kit that allows you to create advanced vision system applications 17
Amazon Sage Maker is AWS's flagship machine learning product 18
it allows you to build and train your own machine learning models and then deploy 19
them to the AWS cloud and use them as a back-end for your applications 20
Amazon Rekognition provides deep learning based analysis of video and images Amazon Lex 21
allows you to build conversational chatbots, these can be used in many 22
applications such as first-line support for customers. Amazon Polly provides 23
natural sounding text-to-speech Amazon comprehend can use deeper learning to 24
analyze text for insights and relationships this can be used for 25
customer analysis or for advanced searching of documents. Amazon Translate 26
can use machine learning to accurately translate text to a number of different languages 27
Amazon Transcribe is an automatic speech recognition service 28
that can analyze audio files that are stored in Amazon s3 and then return that 29
transcribed the text, ok so let's have a look at using one of these machine 30
learning services Amazon recognition for analyzing some image and video 31
machine learning on AWS is one of the coolest things you can do on AWS and 32
Amazon recognition is a service that just absolutely blows my mind away the 33
reason I say that is that 30 years ago I used to be a young engineer and I was 34
working in the industrial robotics industry and back then we had a vision 35
system that could recognize from a black-and-white image the difference the 36
difference between a triangle and a square and back then that was really 37
groundbreaking stuff and I look now over the last 30 years and where we are now 38
compared to where we were back then which is just unbelievable and the 39
potential for this stuff is absolutely beyond belief 40
ok let's get into it, its services and then to Amazon Rekognition it's R for 41
just plain old Rekognition without the Amazon, so we scroll down to Rekognition 42
that'll take us into the Rekognition management console and we'll try a demo 43
so this will take us into a lot of different demonstrations of the power of 44
this Amazon Rekognition so here we can see we've got a sample image of a guy on 45
a skateboard doing a flip and amazon recognition has analyzed this object in 46
this scene in this in this picture and it's identified that there's a 99.2% 47
probability that there's a skateboard there and as the persons a human being 48
is playing a sport we can see a lot more here he's in a parking lot maybe not 49
even a row but close enough there's cars there he's on a road there's buildings 50
so you can see there's a lot of stuff that has been picked up in their image 51
so how does it get this so if we want to use this ourselves we need to send a 52
request to the Amazon Rekognition API or we can do that through the one of the 53
many software development kits that are offered by AWS so if we had for example 54
the Javascript SDK we would have a function there that 55
we could send this request off to. So what does a request look like? so we can 56
see here we're going to have our object that's in a bucket we're going to 57
provide the bucket name and we're also going to provide the name of the object 58
that is our image when we send that off to AWS Rekognition it comes back with a 59
response and here it is it'll come back with all of the labels that it's picked 60
up it's picked up a skateboard with 99.2% confidence and there you can see 61
there's a whole heap of stuff that is a pic that it has picked up from their 62
object and scene detection okay so let's see how it works with an image we give 63
it so we'll click on upload and we'll upload our own image I'm just going to 64
get a picture of an elephant and upload that and so you can see quite quickly 65
it's analyzed that image it's found an animal and elephants and wildlife and 66
that's pretty cool so let's have a look at the other one so I've got image 67
moderation so what this does is it automatically detects explicit content 68
so for something for a a children's side you might want to moderate the images 69
that are being uploaded to that to that side and so this is a great way to do it 70
so we can see here we've got a family a family image so we just click on view 71
content and we can see that it's come back with nothing absolutely nothing so 72
we look at the response and it comes back with moderation labels nothing 73
the reason it came back with nothing is that it's found nothing that is explicit 74
or suggestive adult content so let's have a look at the other one of the girl 75
in the bikini and so there we can see that we have a female swimwear or 76
underwear 98.7 percent probability that that is there and so that's a great tool 77
that you know if you really want to you know look after children if you got a 78
kid's website it's a great way to do it so let's have a look at facial analysis 79
this is quite a good one so we can see there that we've got a girl she's got 80
her sunglasses on but it still recognizes 81
that it's a girl he still recognizes that she's smiling she's wearing 82
sunglasses and there's a whole heap of information her eyes are open even it 83
can tell with sunglasses on I don't know how it does that mouth is open and 84
there's a whole heap of stuff there that it analyzed and we can see here that it 85
can do the same for multiple faces and you can see that there's a male there 86
and so we can select each different one so we can select this one and it says if 87
it appears to be female 100% 11 to 18 years old this one here 23 to 38 years 88
old so it's quite amazing that it can tell the difference in ages of people 89
from an image and it can also identify people so here is where it's identifying 90
from its its database of celebrities we've got Jeff be sauce for it members 91
on and we've got Andy Jasse from AWS and so it's got a hundred percent much 92
confidence here so we're going to do this ourselves I'm just going to upload 93
an image of a couple of celebrities and see whether it can do it for us as well 94
okay so it's it's how to look at that image and it's identified that Keith Urban 95
and Nicole Kidman are in that image with a hundred percent confidence 96
I reckon that's pretty amazing actually but hey that's the whole thing about 97
facial recognition is that we're in a whole different world now that computers 98
can recognize who we are so let's have a look at comparison so here we have an 99
image on the left of a girl and an image of the same girl with a group of other 100
girls and here we can see that it's identified a ninety-eight percent 101
probability that they are the same girl and it's not the other two girls we can 102
do the same with a different image and so here we can see that it's identified 103
that it is a girl on the right let's have a look at another one and it has 104
found that girl as well in the other image and it's and it's identified that 105
she's not the other two girls so again the facial recognition is really quite 106
powerful let's have a look at text and image so here we can see it's picked up 107
text from an image and it's picked up the number plate on a car so obviously 108
there's a lot of stuff that can happen here you know it's obviously you're 109
going to be having a toll on a bridge or something like that great service for 110
doing that let's have a look at video analysis okay so here's a video I'll 111
just play it we can see there's just a video from AWS live and it is picked up 112
that there are two people here and two of them are celebrities are both 113
celebrities so let's have a look at that so we've got those two celebrities and 114
it's identified there we go mr. Vogel's and mr. Bezos and it's also identified 115
some objects in there so it's picked up the furniture and the chair and that 116
someone's wearing it as someone's got a beard and it's picked up quite a bit so 117
that brings us to the end of this little run-through of Amazon recognition and by 118
all means open it up and how to play around with it yourself and try and 119
think about what you can do with it because remember this is if you're a 120
developer you can use this as a back-end by just using the JavaScript SDK which 121
interfaces nicely with this and you can you know that you can come up with some 122
really interesting ideas for applications I'll see you in the next 123
one