all right so i am live with 52 weeks of aws and i'm going to switch gears now and cover
how to prepare for the aws machine learning certification which is a specialty certification
and i've encouraged many students to get this in programs that i've taught at like uc davis
northwestern duke and many students have actually passed the certification and i've worked quite a
bit with aws technology so i think this will be really exciting to talk about and let's get right
into it so i'm going to go ahead and i'm gonna show my screen if you're watching uh from a remote
location uh you can see this if you're listening uh with audio you should be fine as well so to
start off with uh the the main thing i want to cover here is just to get into the logistics of of
what it is that we're we're doing uh and and cover that out and let me just uh make sure that i'm
getting this thing set and give me one second here
i'm just gonna i think i'm gonna go from this this is a good window
to to go from so let's start with uh welcome to machine learning course here
some of the things that are prerequisites to be aware of would be the technical knowledge
knowing a little bit about it technical knowledge knowing some of the cloud foundations material so
for example going through the previous material i did on solutions architect
also knowing about python programming that's really critical i have a lot of information on
python programming at pragmatic ai labs or pi a ml.com and or noahgift.com that's free and
also knowing some basics about statistics that's important to know and also preferred knowledge
would be you know cloud computing so again i would go through the solutions architect material you
know about distributed systems you also have some familiarity with networking concepts and you also
have knowledge of multi-tiered architecture so those are the things to be aware of and what are
we going to cover over the next several weeks i'm going to get into what is machine learning
i'm also going to cover machine learning pipelines using sagemaker i'm going to talk
about managed amazon ml services for forecasting managed amazon ml services for computer vision
i'm also going to talk about managed email services for natural language processing and
the first module is what we're covering right now and next week i'll cover introducing machine
learning the week after that will be machine learning pipeline the week after that we'll be
introducing forecasting the week after that will be computer vision the week after that will be
nlp introducing natural language processing and in week seven we're gonna do the course summary
uh so module two uh if we if we dive into one of the details there it's going to be
what is machine learning the business problems that are involved with machine learning the
machine learning process machine learning tools overview the machine learning challenges module 3
which will be the week after this it will be or we two weeks from now would be scenario introduction
collecting and securing data getting data feature engineering training hosting and using a model
evaluating the accuracy of a model hyperparameter and model tuning module four
will be forecasting so forecasting overview processing time series data using amazon forecast
uh and when we get into computer vision i'll talk about how to think about computer
vision how to analyze image and video how to prepare custom data sets for computer vision
and then in module 6 it will be nlp so overview of natural language processing llp
natural language process processing managed services and then in uh course summary it'll be
really tying everything together talking about some of the amazon documentation that will be
helpful and the requirements of the certification so that's really you know it in a nutshell but
let's talk about some of the machine learning jobs that are available for amazon machine learning
the data science role is one of the roles that is very commonly found in machine learning and
this is applying statistics and analytical skills to interpret data most of the time a data
scientist has i would say graduate degree or above and those degrees are typically in statistics
computer science economics or data science which i teach in many data science programs and some
programming skills as well so that's really the the data scientist role now the machine learning
engineer role which is something that i've been teaching for quite a while as well is a little
more programming and systems design centric and so understanding really the solutions architect
material would be important potentially even the aws developer certification could be helpful
if you have a background as a developer or a software architect that would be very helpful to
get a machine learning engineer role and also to have some understanding of statistics i would say
every human on earth should know statistics but it's especially helpful in machine learning
engineering there's also a science researcher applied science reacher
researcher which would be applying machine learning technology to a specific domain
let's say a data journalist would be a good example of this or potentially a natural
language processing a model creator that could be a good example of applied science researcher
these require knowledge of both the domain and the machine learning also you could see a biologist
could be a good example of this a nutritionist nutrition and machine learning are becoming a big
deal and then there's also the machine learning developer role this would be integrating machine
learning with software applications so this would be maybe using things like hugging face
for example downloading the pre-trained model and putting it into production this requires
strong app development skills and machine learning knowledge so let's also now talk about
some of the resources that are available to study for the awesome machine learning certification
in terms of resources documentation and white papers so to start with we have a
machine learning on aws that would be one of the resources here to to take a look at there's also
the aws machine learning blog there's machine learning solutions in the marketplace there's
also amazon machine learning service documentation there is machine learning solutions in the aws
marketplace there's also amazon machine learning service documentation and machine learning in the
aws partner network these are all great places to take a look at additional information and in
fact one of the things i'll bring up here and i'll just talk about this a little bit if i go over to
the official website and i change gears here and go to that one of the things that i
believe would be a really good resource would be to go to the documentation for
aws and if we go here we can take a look at this documentation and if you look at the documentation
overview they have a methodical view here of all the different services and going down to
the section on the ews website where they cover machine learning and making sure you understand
at least the definition of every service that's available like amazon augmented ai amazon code
guru amazon comprehend amazon devops guru amazon elastic inference amazon forecast etc etc would
be very helpful the really the the center of the universe in terms of machine learning on aws is
amazon sagemaker and it would also be a very good idea to cover the amazon sagemaker documentation
in detail and i'll just briefly talk about it that amazon sagemaker is a fully managed service with
features that help developers and data scientists prepare build train and deploy machine learning
models quickly sagemaker removes heavy lifting from the ml process to make it easier to develop
high qual quality models sagemaker provides the components used for machine learning so
this is really probably in itself one of the best possible pieces of documentation to read
you can assume that if you're going to pass the aws machine learning certification that
you have had experience with machine learning uh doing with sagemaker that you've played around
with the different features including sagemaker studio sagemaker autopilot those kinds of things
you also understand which libraries can be used in a distributed fashion so
for example there are some libraries that allow you to split the training across multiple gpus
also about deploying and managing machine learning models uh this would be really critical so these
are all things to be to really to pay attention to you know potentially as you're studying for the
certification exam and and maybe one final thing that i'll mention is that if you're a student
there is something called sage maker studio lab that i also would recommend you take a look at and
a amazon sagemaker studio lab you can the the url is studio lab s-t-u-d-i-o-l-a-b dot sagemaker.aws
and you can actually get access to this um if you're a student by requesting a free account
and i already have an account so if i log in here uh really the the main takeaway is that
you can actually get free access to gpus and you can start a runtime you can either pick a cpu or
a gpu runtime and there's a whole series of uh sample notebooks and including there's even a
dive into deep learning interactive book that shows you 150 jupiter notebooks and
teaches the ideas uh and the math and the code and this has been adopted at 300 universities
including stanford mit harvard and cambridge so there's a lot of really good stuff here
in addition there's links to hugging face notebooks that you can play around with inside of
studio sagemaker.aws so lots of really helpful resources that are free
included on the aws website and maybe the last thing i'll say that would be
very helpful for you would be to go to the aws certification website and if i pull this up
go to the official website and i go over to the different certifications here and i look at
a particular certification and i look at the different exams that are available and i look at
i'm going to find the machine learning certification when you click on the link
there's all this different information available including the exam guide the sample questions but
the other one that i would highly highly recommend is that if you go first of all go through the
exam review a documentation that they give you and also go to the white papers that they recommend
i would say potentially the white paper itself would be maybe the very first thing that i would
recommend someone read and maybe the last thing that i'll have somebody read if they're studying
for the certification exam just because it sets the proper context and if i go through
here and pull this up we can see that there's something called the machine learning lens and
the machine learning lens is basically a pattern that you can follow called the well architected
framework pattern and it covers the things that we're already used to if you're familiar with
solutions architect like operational excellence security reliability performance efficiency
and cost optimization but also it covers the well architected machine learning life cycle which is
the process of business school identification ml problem framing data processing which would
include data collection data pre-processing future engineering uh model development which
is training tuning evaluation model deployment which is an inference prediction and model
monitoring so these are the life cycles of the machine learning life cycle and
really understanding the way that aws thinks about these things is going to
be critical to your success on the exam and if you look and then tie that again back to
the original well architected framework pillars you can see that performance efficiency covers
deployment and monitoring reliability covers monitoring business goal operational excellence
covers the business goal security covers model framing cost optimization covers you know model
problem framing and also data processing so you can see that there's a link as well uh to
the original where architected framework uh and and so this is a great thing to to be aware of
and it's something that we'll have some time to cover over the next several weeks
as we're going through the aws machine learning certification material
and what i will do then is just maybe briefly talk about that so over the next several weeks
i'm going to go through some of the official training material for aws machine learning
certification walk you through how to study for it prepare for it get into some of the theory as well
potentially do some demos around any of the topics that seem interesting and keep going
down this road of mastering aws all right we'll see everybody next week