AWS SageMaker For ML And DL Tutorial Playlist- What Will We Learn In This Playlist?

hello all my name is krishnak and welcome to my youtube channel so guys uh i'm going to start a new playlist on amazon sage maker where we can actually create train deploy our machine learning models into aws cloud and then we can also create the endpoints of those specific models not only machine learning models guys we can also do with respect to deep learning models and this was requested by many of you people apart from that some of my friends some of my experienced friends who are experienced in data science when they were giving interviews in some of the companies they told that now people the companies are also asking uh amazon sage maker questions like how do you deploy a model in aws sagemaker and all right and probably the reason is that guys now most of the companies are using three main important cloud platforms that is aws azure and google cloud platform these are the most used cloud platforms worldwide in various organization yesterday i was also seeing the net netflix uh you know the data data architect of them and there also they were extensively using aws services like s3 bucket and all and many more things so it is important now to understand how important this knowledge of the cloud platform is required i'll definitely start with aws in the future lectures i'll come with azure and google cloud platform also but here we will be specifically talking about amazon sagemaker apart from this we'll also try to cover some of the important services in aws like s3 bucket will will try to understand what is easy to instance you know and many more things with respect to aw amazon stage maker over here there is something called as amazon sage maker studio will also try to understand how we can actually do our build train and deployment of the models and probably this playlist will be having around 10 to 15 videos first i'll go ahead with ml machine learning projects and deep learning projects then we'll also go ahead with nlp projects now people may be also concerned because uh i had started the deep learning playlist which is about i have to complete the object detection that will also be going parallely guys uh object detection and all we'll be taking the live classes right but this all videos where we are using this cloud platform it will be recorded video so both will be happening parallelly so don't worry about it all the videos will be recorded and now i'm going to speed up the process of uploading the video also now apart from that guys uh you can see over here what is amazon sagemaker it is a cloud a machine learning platform that was launched in november 2017. i have used it it is very very good because amazon sagemaker also has some types of algorithms they have some algorithms which is already trained in their platform you know and you can actually reuse it and it gives you some amazing results you can also train your uh model machine learning models within this platform you can also see that there is something called as labeling jobs you can label your data you can label the whole data sets and many more things so lot of functionalities are there so i will be discussing about that in the upcoming classes but here what all things i am going to cover in this particular playlist that we are going to see first of all we'll just understand about aws services what all services has been provided by aws and what is cloud computing so this is pretty much important the second topic that we will be understanding in aws services one important service is something called as aws s3 bucket so this is pretty much important this is pretty much handy if you don't know about s3 bucket this is this is this is a storage uh place you can even just think of a storage place where all the files will be uploaded all the data sets any kind of files can be uploaded over there uh which acts as a storage unit itself right you need not take or buy any hardware instead you can actually use those s3 bucket and whatever i'm going to teach you over here guys you will definitely require credit card but i will make sure that none of the credit card will get charged only some amount may get charged later in the deep learning when you're training your deep learning models in the gpu that you're using from the basic gpu that you're using in this aws platform now you may be considering krish uh even though if we have gpus why do company actually take this kind of cloud services just understand one thing guys in a company you know there will be many projects that will be fairly running right some will be ml model some will be dl model projects and many more things and every time you can't just buy a gpu or hardware because a lot of cost is involved so what companies do is that they actually take the services from either aws azure or google cloud platform and they pay as per use you know so as as they're using as how much they are using based on that they will actually pay and there are some cost involved but in this playlist whatever we will be discussing i'll make sure that just follow the steps what i will be doing probably you will not get charged okay so if if they if you make some mistakes definitely i i made some mistakes previously i was charged some amount of money in aws okay so don't make that mistakes whatever i tell you in this specific playlist just follow that okay so we'll try to understand aws s3 bucket in the third step what i'm going to make you understand over here is that about sage maker right amazon sage maker this is pretty much important we'll try to understand amazon sagemaker that is aws sagemaker um there are two things in this as i told you over here we'll also understand about amazon stage maker studio right so this is also this there are two platforms you can also create a notebook instance directly see over here there is something called as notebook instance right you can execute your code through this also you can execute your code through this also right so both of them are there if i go with respect to this it will be it will be showing us like this but if i go with respect to the notebook instance what you can do is that you can create the notebook in your normal jupyter notebook in your local and later on you can upload there and you can do all the tasks even similar in the sagemaker studio but we'll try to understand in depth as we go ahead uh after we understand this uh what we are going to do at least one ml project we are going to do it completely from end to end so when i say end to end we will actually create a scalable model okay end to end with deployment so this will be with deployment okay and probably this will be a scalable model okay so make sure that this will be a scalable model um the fifth thing that we'll try to do is that we'll try to understand about the dl project we'll execute a dl project it will be also end to end and we'll try to create this as a scalable model you don't not have to worry about docker kubernetes and all the apis that is actually created by aws with with the selection of some options it will actually create a scalable model itself uh after this uh definitely we will also be doing an nlp project so this will give you an idea about everything uh with respect to this so that you don't have to worry about anything else now once we do this um probably again guys uh when i'm saying one ml project dl project maybe two three also okay i'll be continuing this playlist whenever i find something interesting okay now the seventh thing that we are going to do is that why why we are trying to learn this i i also want to mention this point guys please make sure that you add this thing in your resume also since as i told you that many of the companies are now they're using extensively of this kind of services right like aws sagemaker even my previous company also used to use it right uh previous to previous whatever company i was working it was a product-based company they also moved into aws completely they were initially working into azure then they completely moved into aws and there also they were heavily using this so uh this is things we are going to cover uh first uh probably this particular video in this video i really wanted to show you what all things we will actually try to cover and this will be also completely from end to end till the api creation and then you will be able to see more things now in my next video i'll try to show you how you can create an aws free tire account the free tire account is basically for 12 months so you can actually use some of the services let me show you what all services you can actually use okay so this is the aws free trial account what are the types of offers you can explore more than 60 products and start building on aws using the free tire uh it is 12 months free okay now what are things you can use in the free tire version you can use aws each to ec2 instance of 750 hours per month okay but make sure that whatever instance we are selecting right it should be this instance t2 micro t3 micro this i will tell you okay how where we have to create this instance and all i will show you each and every steps but we are just going through some of the services that are provided in the free trial details as i told you aws s3 bucket it is a secure durable and scalable project storage uh infrastructure you can store any kind of files over there suppose if i'm creating any model i can store in the s3 bucket and i can read it from the aws sagemaker because model file usually is more than 500 mb right one of the problem that you have faced in hiroku if we have some vision models you know it was going more than 50 mb right so at that time we cannot actually store it into a helical platform otherwise 50 or 500 mb uh because there is some storage limit right but here you are getting 5 gb so even though your model is around 2gb you can actually store that in the s3 bucket and you can read it from there okay apart from this uh this is the amazon dynamodb this dynamodb is also a nosql database again i think this is a product of aws itself the functionalities if you know about mongodb probably you'll also be able to work with dynamic db but again i'm not going to cover that probably in some other playlist i'll try to cover this lambda function also i'll try to say some of the bit information about lambda function one million free request you can actually put into the lambda function okay and this is i've worked on a lot in the lambda functions uh in my in my in my from past two years i guess i'm working more in the lambda functions but the main thing will be amazon stage maker so we'll try to perform all the operation into this apart from that there also some more information that you can see over here but just make sure that the main reason why i'm doing this is to basically showcase you that this thing can be very very handy in your resume now let us go towards the next step and we'll try to create an aws free trial account okay so create aws free tire account i everyone i want you all to do this because this is pretty much important okay and this will be for 12 months so first of all go to google and just search for aws free tire okay when you do this you'll be able to see this first option go and just create your account once you create your account just give your email like this anything like this i'm just going to create my account over here okay some password i'm just going to give some password okay and some account name testing one two you can give anything okay it is up to you so i've given all these things as soon as i click on continue see this i'll just click on continue then just go and click on personal thing and just fill up all the details that is required over here once you fill up all the details when you go and click on create account and continue then the next step is that they'll ask you a credit card number remember the credit card will not get charged you know unless and until you use something other than this micro instance i told you in the free version right in the free version i told you you have to use this instance that is t2 micro t3 micro unless until you are using this there will not there not be any charges so don't worry about it just go and go ahead with this and probably just create the account over here and provide your credit card number credit card number is important guys even in aws even in google cloud in google cloud also you get 300 for one years right so there also they'll ask your credit card so make sure that you provide the credit card number and after that just create the account and then probably in the next lesson i will try to show you i'll try to explore about the s3 bucket uh what you can do with respect to s3 bucket how you can store the data how you can do many things how you can call that s3 bucket through your python code and everything in with respect to that we'll try to discuss so we will cover s3 s3 is definitely required we'll we'll also try to cover lambda function in the later stages after completing s3 we will go into amazon series maker and then we'll try to create some ml projects okay so yes this was all about this particular video please do subscribe the channel uh share with all your friends because i am starting this aws sage makeup playlist it will be for somewhere around 10 to 15 videos and every day i will be uploading at least one to two videos okay so here uh i'll see you all in the next video uh please do subscribe the channel guys thank you all bye

Channel: Krish Naik

Views: 128,327

Keywords: aws sagemaker data science, machine learning, deep learning, data cience

Id: LkR3GNDB0HI

Length: 13min 17sec (797 seconds)

Published: Wed Aug 26 2020