Introduction to Analytics and Machine Learning

BackSpace Academy
A free video tutorial from BackSpace Academy
The fastest route to cloud certification.
4.5 instructor rating • 3 courses • 261,111 students

Learn more from the full course

Amazon Web Services (AWS) Certified - 4 Certifications!

Videos, labs & practice exams - AWS Certified (Solutions Architect, Developer, SysOps Administrator, Cloud Practitioner)

43:56:47 of on-demand video • Updated December 2020

  • You will be fully prepared for the AWS Certified Solutions Architect Associate, AWS Certified Developer Associate and AWS Certified SysOps Administrator Associate exams.
English Welcome back to backspace Academy. In this lecture we're going to be running through some of the analytics and machine learning services on AWS Amazon Elastic MapReduce or EMR for short is AWS's Hadoop framework as a service you can also run other frameworks in Amazon EMR that integrate with Hadoop such as Apache spark HBase Presto and Flink. Data can be analyzed by EMR in a number of AWS data stores including Amazon s3 and Amazon DynamoDB Amazon Athena allows you to analyze data stored in an Amazon s3 bucket using standard SQL statement. Amazon Elastic search is a fully managed service for's elasticsearch framework this allows high-speed querying and analysis of data that is stored on AWS. Amazon Kinesis allows you to collect process and analyze real-time streaming data. Amazon quicksight is a business intelligence reporting tool similar to tableau or if you're a Java programmer similar to BIRT and is fully managed by AWS so let's have a look at some of the machine learning services on AWS AWS deeplens is a deep learning enabled video camera, it has a deep learning software development kit that allows you to create advanced vision system applications Amazon Sage Maker is AWS's flagship machine learning product it allows you to build and train your own machine learning models and then deploy them to the AWS cloud and use them as a back-end for your applications Amazon Rekognition provides deep learning based analysis of video and images Amazon Lex allows you to build conversational chatbots, these can be used in many applications such as first-line support for customers. Amazon Polly provides natural sounding text-to-speech Amazon comprehend can use deeper learning to analyze text for insights and relationships this can be used for customer analysis or for advanced searching of documents. Amazon Translate can use machine learning to accurately translate text to a number of different languages Amazon Transcribe is an automatic speech recognition service that can analyze audio files that are stored in Amazon s3 and then return that transcribed the text, ok so let's have a look at using one of these machine learning services Amazon recognition for analyzing some image and video machine learning on AWS is one of the coolest things you can do on AWS and Amazon recognition is a service that just absolutely blows my mind away the reason I say that is that 30 years ago I used to be a young engineer and I was working in the industrial robotics industry and back then we had a vision system that could recognize from a black-and-white image the difference the difference between a triangle and a square and back then that was really groundbreaking stuff and I look now over the last 30 years and where we are now compared to where we were back then which is just unbelievable and the potential for this stuff is absolutely beyond belief ok let's get into it, its services and then to Amazon Rekognition it's R for just plain old Rekognition without the Amazon, so we scroll down to Rekognition that'll take us into the Rekognition management console and we'll try a demo so this will take us into a lot of different demonstrations of the power of this Amazon Rekognition so here we can see we've got a sample image of a guy on a skateboard doing a flip and amazon recognition has analyzed this object in this scene in this in this picture and it's identified that there's a 99.2% probability that there's a skateboard there and as the persons a human being is playing a sport we can see a lot more here he's in a parking lot maybe not even a row but close enough there's cars there he's on a road there's buildings so you can see there's a lot of stuff that has been picked up in their image so how does it get this so if we want to use this ourselves we need to send a request to the Amazon Rekognition API or we can do that through the one of the many software development kits that are offered by AWS so if we had for example the Javascript SDK we would have a function there that we could send this request off to. So what does a request look like? so we can see here we're going to have our object that's in a bucket we're going to provide the bucket name and we're also going to provide the name of the object that is our image when we send that off to AWS Rekognition it comes back with a response and here it is it'll come back with all of the labels that it's picked up it's picked up a skateboard with 99.2% confidence and there you can see there's a whole heap of stuff that is a pic that it has picked up from their object and scene detection okay so let's see how it works with an image we give it so we'll click on upload and we'll upload our own image I'm just going to get a picture of an elephant and upload that and so you can see quite quickly it's analyzed that image it's found an animal and elephants and wildlife and that's pretty cool so let's have a look at the other one so I've got image moderation so what this does is it automatically detects explicit content so for something for a a children's side you might want to moderate the images that are being uploaded to that to that side and so this is a great way to do it so we can see here we've got a family a family image so we just click on view content and we can see that it's come back with nothing absolutely nothing so we look at the response and it comes back with moderation labels nothing the reason it came back with nothing is that it's found nothing that is explicit or suggestive adult content so let's have a look at the other one of the girl in the bikini and so there we can see that we have a female swimwear or underwear 98.7 percent probability that that is there and so that's a great tool that you know if you really want to you know look after children if you got a kid's website it's a great way to do it so let's have a look at facial analysis this is quite a good one so we can see there that we've got a girl she's got her sunglasses on but it still recognizes that it's a girl he still recognizes that she's smiling she's wearing sunglasses and there's a whole heap of information her eyes are open even it can tell with sunglasses on I don't know how it does that mouth is open and there's a whole heap of stuff there that it analyzed and we can see here that it can do the same for multiple faces and you can see that there's a male there and so we can select each different one so we can select this one and it says if it appears to be female 100% 11 to 18 years old this one here 23 to 38 years old so it's quite amazing that it can tell the difference in ages of people from an image and it can also identify people so here is where it's identifying from its its database of celebrities we've got Jeff be sauce for it members on and we've got Andy Jasse from AWS and so it's got a hundred percent much confidence here so we're going to do this ourselves I'm just going to upload an image of a couple of celebrities and see whether it can do it for us as well okay so it's it's how to look at that image and it's identified that Keith Urban and Nicole Kidman are in that image with a hundred percent confidence I reckon that's pretty amazing actually but hey that's the whole thing about facial recognition is that we're in a whole different world now that computers can recognize who we are so let's have a look at comparison so here we have an image on the left of a girl and an image of the same girl with a group of other girls and here we can see that it's identified a ninety-eight percent probability that they are the same girl and it's not the other two girls we can do the same with a different image and so here we can see that it's identified that it is a girl on the right let's have a look at another one and it has found that girl as well in the other image and it's and it's identified that she's not the other two girls so again the facial recognition is really quite powerful let's have a look at text and image so here we can see it's picked up text from an image and it's picked up the number plate on a car so obviously there's a lot of stuff that can happen here you know it's obviously you're going to be having a toll on a bridge or something like that great service for doing that let's have a look at video analysis okay so here's a video I'll just play it we can see there's just a video from AWS live and it is picked up that there are two people here and two of them are celebrities are both celebrities so let's have a look at that so we've got those two celebrities and it's identified there we go mr. Vogel's and mr. Bezos and it's also identified some objects in there so it's picked up the furniture and the chair and that someone's wearing it as someone's got a beard and it's picked up quite a bit so that brings us to the end of this little run-through of Amazon recognition and by all means open it up and how to play around with it yourself and try and think about what you can do with it because remember this is if you're a developer you can use this as a back-end by just using the JavaScript SDK which interfaces nicely with this and you can you know that you can come up with some really interesting ideas for applications I'll see you in the next one