Introduction - Vertex AI for ML Operations
Summary
TLDRIn this introductory video, Mike, a Google statistician, likens machine learning workflows to assembling jigsaw puzzles, emphasizing their uniqueness and importance in efficiency. He plans to share various workflows through a series of videos and a GitHub repository, featuring end-to-end machine learning projects using Google Cloud's Vertex AI. Mike encourages viewers to explore all videos for new insights and suggests learning resources for those interested in machine learning, inviting feedback and collaboration to improve the shared knowledge base.
Takeaways
- 🧩 The video uses the analogy of jigsaw puzzles to discuss the uniqueness and personalization in machine learning workflows.
- 🔍 Mike, a Google statistician, aims to share and explore different machine learning workflows in this series of videos.
- 📚 He emphasizes the importance of learning from others' workflows to improve one's own efficiency in machine learning projects.
- 🌐 The series will cover workflows using Google Cloud and Vertex AI, focusing on their integration and application in end-to-end machine learning projects.
- 📘 Content will be based on a GitHub repository containing Jupyter notebooks, which is a common tool in the machine learning community.
- 🛠️ The series will guide viewers on setting up a Google Cloud environment, sourcing data, and using various machine learning methods including AutoML, custom training, and BigQuery ML.
- 🎓 Mike recommends resources for learning machine learning, including Google's Machine Learning Crash Course, Andrew Ng's course on Coursera, and Grant Sanderson's (3Blue1Brown) YouTube playlists.
- 🔑 The video series is designed to be modular, allowing viewers to skip around based on their interests, but Mike encourages watching all to gain a comprehensive understanding.
- 🤖 The series will not delve into the low-level details of machine learning algorithms but will focus on connecting process steps to form efficient workflows.
- 💡 Mike invites feedback and suggestions for additional workflows or improvements, leveraging the GitHub repository's issue feature for community collaboration.
- 👍 He concludes by encouraging viewers to like, subscribe, and comment to show support and contribute to the community.
Q & A
What is the main theme of the video series presented by Mike?
-The main theme of the video series is sharing machine learning workflows using Google Cloud and Vertex AI, with a focus on the unique approaches people take to solve problems, similar to how different people approach jigsaw puzzles.
Why does Mike compare machine learning workflows to jigsaw puzzles?
-Mike compares machine learning workflows to jigsaw puzzles to illustrate the unique and personalized nature of problem-solving approaches in both scenarios, and to emphasize the value of learning from different techniques.
What does Mike find most interesting about observing others build puzzles?
-What Mike finds most interesting is observing the different techniques used by others, which can inspire him to try new methods and potentially become more efficient at tackling puzzles.
What is the purpose of the GitHub repository mentioned in the script?
-The GitHub repository serves as a collection of Jupyter notebooks that demonstrate end-to-end machine learning workflows using Google Cloud and Vertex AI, allowing viewers to follow along and learn from the examples provided.
Why does Mike emphasize the importance of learning from others' workflows?
-Mike emphasizes the importance of learning from others' workflows because they reflect the accumulated knowledge and efficiency strategies of individuals, which can help one quickly improve their own practices in the field of machine learning.
What are the different methods Mike plans to cover in the video series?
-Mike plans to cover methods such as no-clicking no-coding model building and deployment, custom training, using BigQuery's built-in machine learning, and working with TensorFlow, among others.
What does Mike suggest for someone who wants to learn more about machine learning?
-Mike suggests starting with a high-level overview from the Google Machine Learning Crash Course, then watching Grant Sanderson's video series on neural networks, and finally taking the Machine Learning course from Stanford on Coursera for a deeper understanding of the fundamentals.
What is the structure of the video series according to the transcript?
-The video series is structured into multiple parts, starting with setting up the environment, followed by data sourcing, and then covering various methods of model training and deployment, including no-clicking no-coding, custom training, and using different tools like TensorFlow and BigQuery ML.
How does Mike plan to make the video series accessible and modular?
-Mike plans to make the video series modular by ensuring each video starts with prerequisites, allowing viewers to skip around or focus on specific parts that interest them, while still being able to follow along with the workflow.
What is the role of the GitHub repository in relation to the video series?
-The GitHub repository is where the Jupyter notebooks corresponding to each video are hosted, providing a practical, interactive way for viewers to engage with the workflows demonstrated in the series.
How does Mike encourage viewer engagement and feedback?
-Mike encourages viewer engagement by asking viewers to like, subscribe, and comment on the videos, as well as to use the GitHub repository's issues feature to provide feedback, suggest improvements, or start discussions.
Outlines
🧩 Introduction to Machine Learning Workflows
In this introductory paragraph, Mike, a Googler and statistician, sets the stage for a series of videos on machine learning workflows. He uses the analogy of assembling jigsaw puzzles to illustrate the unique and personal nature of workflows in machine learning. He explains that, much like puzzle assembly techniques vary among individuals, so do the workflows in machine learning. Mike's passion for learning and sharing is evident as he invites viewers into his office and introduces the concept of exploring different techniques to improve efficiency. The video series aims to share workflows observed in the field, with the goal of helping viewers incorporate new methods into their future projects. The content will be hosted on a GitHub repository, featuring Jupyter notebooks as a common platform for machine learning work.
🛠️ Setting Up the Machine Learning Environment
Mike outlines the structure of the video series, which begins with setting up a Google Cloud environment in the first video. He plans to introduce a data source and discuss the selection process in the following videos. The series will cover various methods of training and deploying machine learning models, including no-click procedures, coding with Python, and higher-level orchestration. Mike emphasizes the importance of understanding the core components of Vertex AI and Google Cloud Platform to tailor workflows to individual needs. He also addresses common questions, such as the scope of the series, the order of watching the videos, and how to learn machine learning. He suggests that while the series won't delve into the minutiae of every machine learning method, it will focus on connecting process steps to form efficient workflows.
📚 Recommended Learning Resources for Machine Learning
In this paragraph, Mike shares his favorite resources for learning machine learning, acknowledging that the series is not a comprehensive tutorial on the subject. He recommends starting with Google's 'Machine Learning Crash Course' for an overview of terminology and problem framing. Next, he suggests the 'Machine Learning' course by Andrew Ng on Coursera for a deeper understanding of algorithms. Lastly, he points to YouTube playlists by Grant Sanderson of '3Blue1Brown' for visually engaging explanations of neural networks and foundational math concepts. Mike encourages viewers to engage with the content, provide feedback, and use the GitHub repository for ongoing collaboration and updates to the shared workflows.
Mindmap
Keywords
💡Jigsaw Puzzles
💡Machine Learning
💡Workflows
💡Google Cloud
💡Vertex AI
💡Automated Procedures
💡Custom Training
💡BigQuery
💡TensorFlow
💡GitHub Repository
💡Jupyter Notebooks
💡Orchestration
Highlights
Mike, a Googler and statistician, shares his passion for learning and sharing in the context of machine learning workflows.
Jigsaw puzzles are used as a metaphor for the diverse approaches to solving problems in machine learning.
Workflows in machine learning are as unique as fingerprints, reflecting individual experiences and efficiencies.
The importance of learning from others' workflows to improve one's own efficiency in machine learning projects.
Introduction of a series of videos focusing on sharing workflows in machine learning using Google Cloud and Vertex AI.
The video series will cover end-to-end machine learning projects, showing different techniques and tools.
The series will be structured to allow viewers to skip around based on their interests.
GitHub repository containing Jupyter notebooks will be the central resource for the video series.
Explanation of how to set up a Google Cloud environment for machine learning.
Discussion on selecting a data source and making it available for various training methods.
Introduction to using automated procedures like AutoML for training and deploying models.
Use of custom training, BigQuery's built-in machine learning, and TensorFlow in the series.
Visual aids will be provided to help explain the workflows and processes in the videos.
The series will not cover very low-level details to maintain focus on connecting process steps in workflows.
Viewers are encouraged to watch all videos for a comprehensive understanding, but each video will have prerequisites listed.
Mike's recommendations for learning machine learning, including courses and YouTube playlists.
Invitation for feedback and collaboration through GitHub issues to improve and expand the repository.
Emphasis on the collaborative nature of AI and ML, aiming to make the field more approachable and well-connected.
Transcripts
welcome i'm mike a googler a
statistician and i'm passionate about
learning and sharing today i'm a friend
and i welcome you
in
to my office
here we are
i want to start this series with a
little story
it involves these you probably recognize
these these are jigsaw puzzle pieces uh
jigsaw puzzles are a big part of my
family these two pieces go to
this gigantic puzzle that we're working
on right now has
more than 2 000 pieces
and i bring this up for a reason
i find that there's a lot of things that
we do in common when different people
build jigsaw puzzles maybe we aim for
the corner pieces
maybe we fill in the edges because the
pieces are a little easier to identify
they have a flat side depending on the
cut of the puzzle
and it's a little easier to frame and
see the size of what you're about to
work on but when you go to the middle my
preferred method is looking for objects
that have like clear lines and then i
can pick those pieces uh
my wife she likes to pick ones where
like sections with a color that's easy
to identify amongst all the pieces
i find that when i work with my mom
she takes the approach of kind of inside
out she looks for features and she
builds upon those and then connects them
to adjacent features
but what's most interesting about
watching family members and others build
puzzles is i see techniques that aren't
my own
and then when i get a new puzzle i might
consider trying a different technique
that might help me or make me more
efficient at that particular puzzle
why are we talking about puzzles you
clicked on a link that's related to
machine learning and google cloud with
vertex ai well here's why
i believe
workflows are the most important part of
how we do
our work and in machine learning
workflows are almost as unique as a
fingerprint
my favorite thing to do when i go to
conferences or meet new people who are
in the same field is ask them
how do you do your work and why do you
do it that way i remember being very
very young as a statistician and going
to
uh the joint statistical meeting the
largest gathering of statisticians in
the world and whenever i'd meet someone
the first thing i'd say is what software
do you use how do you use it why do you
use it that way and i would hopefully
get them to show me on their computer at
the time
how they did their job and that made me
an infinitely better statistician very
very quickly because
the workflows are like the mirrors of
what they've learned throughout their
career to make themselves efficient and
i can immediately assimilate and decide
do i want that do i want to take parts
of that do i want to consider that when
a new project comes up where that might
fit better than my typical way and that
is what this series of videos is going
to be about it's going to be about
sharing workflows
that
i have seen with customers with friends
my own curiosity
and i'm going to put them together in
end-to-end pieces that show like a
machine learning project
and share it with you i'm going to try
to make them independent so that you can
skip around or only visit the ones that
seem to match what you're interested in
but i encourage you to try visiting all
of these and see
maybe it offers something that you can
incorporate in one of your future
projects
so let's take a look at a few things
here
this is all going to be based on a
github repository i put it in the lower
screen here
um
let's actually make that bigger so we
can all see it
all right
in this
series it's a github repo made up of
jupiter notebooks a common way that we
work in machine learning
you can visit these notebooks directly
in this repository it's linked in the
description of the video if you ended up
at the video first
maybe you ended up at the repository and
you clicked the video here and that's
the one you're watching
within here i'm going to
tell you how to set up a google cloud
environment that's the next video i'm
going to
bring in a data source and make it
available for all the different training
methods that we're going to use
and i'll talk about why i picked that
one and how others might work that'll be
the next video after that one
and then we will start going through
using automated procedures like automl
to train a model and deploy a model
we'll use custom training
we'll even use bigquery's built-in
machine learning for part of this we'll
use tensorflow
as you go through these i've put up some
visuals that i'm going to use in the
videos to help explain what i'm doing uh
one of my favorites is this one i'm
going to zoom in a little bit here and
let's take a look
let's see
each of these columns is one of these
workflows uh video zero that's setting
up the environment one is the data
source and then this starts with two
there are two uh three videos in that
are labeled two a b and c
the first is going to use no clicking no
coding it's it are all clicking no
coding we're gonna go to a console and
we're gonna build a model and deploy a
model and get predictions all directly
through the web browser in the second
one we're going to replicate the same
thing but we're going to actually write
code from python to orchestrate all the
same things that we clicked with in the
first one and the third
we're going to bottle up that code into
a higher level abstraction called
orchestration
and we're going to kind of like build a
treadmill or a people mover at the
airport to just automatically move
through a lot of the process steps
then videos three those have two a and b
we're going to look at completely
working inside bigquery and then in the
second of those videos we're gonna
extract the model from bigquery and
bring it to vertex and deploy it
then
with four and five and on we're gonna
take custom modeling we're gonna take
tensorflow we're gonna take scikit-learn
we're gonna take
a number of other tools and we're going
to
train models and deploy models but we're
gonna use the core components of vertex
ai and google cloud platform to show
that it enables whatever fingerprint of
a workflow you want
before we get going further
there's probably some common questions
that people ask at this point so let's
do some q a
how about this question first hi mike
what is not covered in these workflows
what's not covered is going to be
uh very low level details i love details
um frequent feedback i get is maybe back
off of the detail and you're probably
going to give me that feedback as well
what i want to do is take us to the
point where all the process steps
connect and focus on the details of
connecting those to create workflows but
once you're inside of a process step
like machine learning we might not go
into all the different methods and all
the different architectures and all the
details of how to perfect an
architecture for a particular data type
we'll back off of that maybe save that
for a different video series so that we
can focus in our workflows here
do i have to watch all the videos does
order matter
uh i would encourage you to watch them
all because that's how we assimilate new
ideas you might be bored in a few maybe
it's like not interesting to your
particular field or how you work but
it's still exposure to something that
matters to someone somewhere that's why
i put it here
it just
maybe exposes you to a new tool that
you'll use but if you don't if you go to
any additional video in the series the
first thing i'll start with is
prerequisites which previous ones are
previous notebooks with videos
you might need to get to that point and
run it
but i've always made the path to each
one as short as possible
how do i learn machine learning
this is my favorite question uh
unfortunately it's not what this video
series is about but i have an answer for
you at least my answer
this is the best part about living right
now is if you ask this question 20 years
ago
both the field wasn't quite as mature
but the availability of great content
like youtube and coursera and
pluralsight and a million other sources
they just weren't out there and now
they're everywhere so i'm going to give
you my favorites let's jump over to the
screen
and let's scroll down in the repository
to a section i added in to the readme
called exactly this learning machine
learning
okay so it's made up of three sources
again opinionated
but these are my favorites and i've
shared them with people who've given me
feedback who are like mike this really
really helped now you're gonna you may
be coming from i've had linear algebra
in calculus and i get all this stuff i
know how it works
still peruse these because if you're
like me you you love things that confirm
your knowledge and these will do that
and they will also give you great ways
of showing how to explain it to others
because these are master teachers in
here way better than me
um the first is a course from google
called the machine learning crash course
this is great for terminology in
explaining how to identify an ml problem
how to frame that problem exposes at a
high level to a lot of different methods
for supervised and unsupervised learning
it references tensorflow apis
so it gives you ability to quickly go
hands-on with some maybe example data
it doesn't go into a lot of details
about the mechanics of what's happening
underneath
and that's why we go to number two on
this list and that's uh
my favorite course i think somebody told
me this is how coursera started was this
course i hope that's true but this is a
andrewing
it's offered by stanford on coursera and
it's just called machine learning and it
takes you through a few weeks of
taking a couple of algorithms and hand
coding like building them
you will understand how these work after
doing this yeah you might complain about
software choice uh octave you you might
not like that part but it's perfect just
do it exactly the way they explain it
and
use it just as a chance to just absorb
information about how things work
now
the third thing i put on here is a list
of
three youtube playlists from youtuber
grant sanderson with channel called
three blue one brown this
grant is a master explainer of very
advanced information these playlists are
my favorite i rewatched them
occasionally the first is a neural net
playlist it has four videos it'll take
you through what a neural net is a
simple one
it will create it he does everything
very visually has his own package to do
those
he even gets into the fourth video the
calculus of back propagation
that is the moment where the light bulb
comes on for most people like when they
understand
how the neural net actually learns the
gradient descent the minimizing of a
loss function how does that
mathematically work it's beautiful
now as he gets into that video uh
you might be begging for a refresher if
you're not up to speed uh relevant
currently relevant on linear algebra and
calculus he's also got two great
playlists for those and i put those in
here
now the order of these i recommend
getting the high level from the google
crash course machine learning crash
course
going and watching the video series with
grant
then going and getting the fundamentals
from the machine learning course
stanford via coursera
and then re-watching those videos from
grant the light bulb just comes on like
i smile when i watch these i wish i was
as brilliant as these people at just
teaching and explaining information
i think you'll like it uh and if you
have a better answer give that to me in
the comments or an email because
i love this stuff i love learning but i
also like watching how other people
teach so i can get better at it
all right let's jump back to q a i think
we have one more question
what if i want more detail or i have
questions
uh
this is great this brings us kind of to
the the wrap up here
uh you're gonna have
a feeling after you watch this video if
you like it hit the like button if you
dislike it
hit the dislike button uh try to hit the
like button
that's like giving me a cookie gives me
encouragement to keep making these
videos and adding more notebooks to this
repository um
[Music]
additional thing if you want more
content like this hit the subscribe
button that lets me know hey people are
waiting on this kind of stuff like make
some more
and i'll even reference others if i see
subscribers hitting this channel
if you want to be alerted when i launch
a new video hit the bell also
now the next
every youtuber says those things
i'm not a youtuber
did this help you
that's like giving a cookie not just to
me but to others who are watching the
video leave a comment say hey this video
helped me this way a little word of
affirmation it gives encouragement to
others who are watching
but the third thing maybe the most
important part is you have feedback hey
here's an additional workflow that i
like i would love for you to include
something like that
i have an improvement like something
you've done could be better i have a
correction something you did wasn't
quite right or you misspoke okay give me
that as well but here's a great thing
since we're using a github repository go
over there click the issues button at
the top start a new issue we'll have a
conversation maybe we'll collaborate and
create but that way the repository can
be updated way faster than i can remake
one of these videos and that will make
the information relevant to everyone
almost immediately
and with that i just want to say thank
you for hanging in here for this intro
video thank you for your time thank you
for your feedback that's going to be
coming let's work together to make the
practice of ai and ml more collaborative
more accurate
more approachable
to a wider and more well-connected
audience have a great day
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