AZ-900 Episode 16 | Azure Artificial Intelligence (AI) Services | Machine Learning Studio & Service
Summary
TLDRThis video script delves into Azure's artificial intelligence offerings, focusing on machine learning services and the Azure Machine Learning Studio. It explains AI basics, the machine learning process, and how Azure Machine Learning aids in building, training, and deploying models as web services. The script highlights features like Automated ML, visual designer, and pipelines for end-to-end model management, showcasing Azure's comprehensive, cloud-based platform for machine learning model development.
Takeaways
- đ§ AI is a branch of computer science that simulates human intelligence and capabilities, while Machine Learning (ML) is a subset of AI focused on teaching software to make predictions based on data.
- đ Azure Machine Learning is a key service in Azure for building, training, validating, and deploying ML models as web services.
- đ The process of building an ML model involves training, packaging, validating, and deploying, with Azure Machine Learning providing tools to assist in each step.
- đ Azure Machine Learning Studio is a web-based interface that allows for the management of the entire Azure ML service, including notebooks, Automated ML, and a visual designer.
- đ» Notebooks in Azure ML can be written in Python or R, offering a flexible environment for creating and executing ML scripts.
- đ§ Automated ML (AutoML) simplifies the process by automatically testing various algorithms on the provided data to find the best model.
- đš The Visual Designer in Azure ML enables building ML models through a drag-and-drop interface without writing any code.
- đ Pipelines in Azure ML allow for the orchestration of the entire ML process from training to deployment, streamlining the workflow.
- đïž Asset management in Azure ML includes datasets, experiments, pipelines, models, and endpoints, providing a centralized way to manage all components of the ML lifecycle.
- đ„ïž Compute resources in Azure ML can be managed for training and deploying models, abstracting away the underlying infrastructure concerns.
- đ Azure ML integrates with other Azure services such as Azure Blob Storage and Azure File Share for data storage and management.
Q & A
What is the main focus of the video script?
-The main focus of the video script is to discuss artificial intelligence products in Azure, particularly the Azure Machine Learning service and how it can be used to build, train, and deploy machine learning models.
What is the relationship between AI and machine learning as described in the script?
-In the script, AI is described as a branch of computer science that simulates human intelligence, while machine learning is a subcategory of AI where software is taught to make predictions and draw conclusions based on data.
What is the role of 'building a model' in the context of machine learning?
-Building a model in machine learning involves teaching the software to analyze data and make predictions. This process includes training the model with data, validating its accuracy, and potentially retraining it to improve results.
How does Azure Machine Learning assist in the machine learning process?
-Azure Machine Learning provides a set of tools that assist in the entire process of building a machine learning model, including training, packaging, validation, deployment, monitoring, and retraining, as well as managing compute resources and automating the selection of algorithms.
What is the significance of 'AutoML' in Azure Machine Learning?
-AutoML in Azure Machine Learning is an automated process that allows users to apply various algorithms to their data to determine which one performs best, and then deploy that algorithm as a designated web service.
What is a 'pipeline' in the context of Azure Machine Learning?
-In Azure Machine Learning, a pipeline is an end-to-end solution that allows users to build, manage, and execute the entire process of training, deploying, and managing machine learning models, whether using notebooks, designer, or automated tools.
How does the Azure Machine Learning Studio differ from the older Machine Learning Studio?
-The Azure Machine Learning Studio is a web-based visual interface for managing the entire Azure Machine Learning service, offering additional features and an updated experience compared to the older Machine Learning Studio, which is no longer actively developed.
What are some of the tools provided by Azure Machine Learning for building machine learning models?
-Azure Machine Learning provides tools such as Jupyter notebooks written in Python or R, a visual designer for drag-and-drop model building, and Automated ML for algorithm selection and parameter tuning.
What is a 'compute target' in Azure Machine Learning, and why is it needed?
-A compute target in Azure Machine Learning is a virtual machine or other compute resource that is used to run the machine learning workflow. It is needed to perform tasks such as training and scoring the model without the user having to manage the underlying infrastructure.
How does asset management work in Azure Machine Learning?
-Asset management in Azure Machine Learning involves managing various components such as datasets, experiments, pipelines, models, and endpoints. These assets are tied together in the workspace and can be used to track the machine learning process and deployment of models.
What are the key features of Azure Machine Learning service mentioned in the script?
-The key features of Azure Machine Learning service mentioned in the script include notebooks, Automated ML, a visual designer for building machine learning pipelines without coding, management of data and compute resources, and integration into machine learning pipelines for orchestration of tasks.
Outlines
đ€ Introduction to Azure AI and Machine Learning
This paragraph introduces the topic of artificial intelligence (AI) products in Azure, focusing on the machine learning service and studio. It explains AI as a branch of computer science that simulates human intelligence and machine learning as a subcategory that enables software to make predictions based on data. The key process in machine learning is building models, which is facilitated by Azure Machine Learning. The paragraph outlines the process of training, validating, deploying, and monitoring machine learning models, and highlights the tools provided by Azure, such as notebooks, a visual designer, and AutoML for automated algorithm selection. It also introduces the concept of pipelines for end-to-end machine learning model development.
đ End-to-End Machine Learning with Azure
The second paragraph delves into the practical aspects of using Azure Machine Learning Studio for creating, managing, and publishing machine learning models. It describes the Azure Machine Learning workspace as a central resource that ties together compute resources, permissions, runs, pipelines, experiments, and model deployments. The paragraph also distinguishes the new Azure Machine Learning service from the older 'Machine Learning Studio,' which is no longer actively developed. Key features highlighted include the use of notebooks for Python or R, Automated ML for algorithm selection and parameter tuning, and the visual designer for building machine learning pipelines without coding. The paragraph concludes with a mention of asset management and other features for managing data and compute resources, as well as orchestrating model training and deployment through machine learning pipelines.
Mindmap
Keywords
đĄArtificial Intelligence (AI)
đĄMachine Learning
đĄAzure Machine Learning
đĄModel Training
đĄWeb Services
đĄAutomated Machine Learning (AutoML)
đĄMachine Learning Studio
đĄNotebooks
đĄVisual Designer
đĄPipelines
đĄAsset Management
đĄCompute Resources
đĄData Stores
Highlights
Introduction to artificial intelligence products in Azure, focusing on machine learning service and studio.
AI is a branch of computer science that simulates human intelligence, with machine learning as a subcategory for predictive analysis.
Azure Machine Learning is a key service for building machine learning models through training, validation, and deployment.
The process of building a machine learning model includes training, packaging, validating, and deploying as web services.
Azure Machine Learning provides tools such as Python or R notebooks and a visual designer for model building.
Automated Machine Learning (AutoML) allows for algorithm selection and parameter tuning to find the best model.
Machine learning models can be managed through Azure's compute resources without worrying about infrastructure.
Introduction to Azure Machine Learning pipelines for end-to-end model building processes.
Demonstration of navigating to the Azure Machine Learning workspace and launching the studio.
Explanation of using notebooks for creating scripts or executing provided samples for model building.
Overview of AutomatedML for experimenting with different algorithms and parameters to achieve the best model score.
Introduction to the Visual Designer for building machine learning models with a drag-and-drop interface.
Asset management in Azure Machine Learning includes datasets, experiments, pipelines, and model endpoints.
Management of compute resources for training and deploying models, and data stores for connecting to data sources.
Live demonstration of building a machine learning model using the Visual Designer with a simple workflow.
Explanation of creating a compute target for running the workflow and selecting it for model training.
Real-time UI demonstration showing the model building process and evaluation results.
Summary of Azure Machine Learning as an end-to-end cloud-based platform for managing ML models.
Differentiation between the old Machine Learning Studio and the new Azure Machine Learning service.
Key features of Azure Machine Learning Service including notebooks, AutomatedML, and Visual Designer.
Management of data and compute resources, and orchestration of model training and deployment through pipelines.
Information about where to find materials for the episode and a teaser for the next episode on serverless computing in Azure.
Transcripts
hello everyone welcome back again after
big data we can move to artificial
intelligence products in azure
and that's the focus of our today's
episode stay tuned
[Music]
for the ai today we'll be talking about
machine learning service and machine
learning studio
but before we move to those solutions
let's talk about
ai in general ai is a branch of computer
science where we use our software
to simulate human intelligence and
capabilities
whereas machine learning is a
subcategory of ai
where we use that software and we teach
it to draw some conclusions and make
some predictions based on
our data and the process of teaching the
software to do that
is called building a model and the key
service to do that in azure is called
azure machine learning
typically the process of building a
machine learning model consists of
training the model based on our data
then packaging and validating that model
if we are happy with the results we can
deploy those models as a web services
then monitoring those web services and
retraining the model
to get even better results and azure
machine learning is here to help us with
this entire process by providing us with
set of tools
tools like notebooks written in python
or r
or a visual designer which allows us to
build machine learning models
using a simple drag and drop experience
directly in our browsers
additionally machine learning models
allows us to manage all the compute
resources where we train
package validate and deploy those models
so that we don't have to worry about
azure
infrastructure and underlying resources
ourselves
additionally azure machine learning
comes with something called automl
this automated process allows us to drop
random algorithms at our data
and see which one scores the best and
deploy that as our designated web
service
and lastly there is also a feature of
pipelines which allows us to build this
entire process
end-to-end whether we are using those
notebooks designer or auto tools
this is complete end-to-end solution for
building machine learning models
let me quickly present to you how azure
machine learning works
first i will navigate to my resource
group called az900
ml inside of this resource group i have
my machine learning workspace resource
which allows me to manage machine
learning service and all its components
in azure portal there's not a lot of
things we can do there are very few
blades when it comes to management of
machine learning workspace
in order to go to machine learning
workspace we need to hit on the launch
studio button
or hit this url here above so let's
click on launch studio button which will
open an
azure machine learning studio this is a
web-based
visual interface for management of
entire azure machine learning service
for example in an outdoor section you
have notebooks automated ml and
visual designer but let's go to the
notebook first
this is a workspace where you can create
your own scripts
or instead you can try out some samples
provided by microsoft
navigate to any notebook that you like
read the tutorial and
execute the provided samples and you
will build your machine learning model
in no time
or instead of writing scripts try
automatedml which allows you to throw
random algorithms at your data
tweak some parameters and see which
model scores the best
and deploy that as a web service and if
this wasn't enough you also have
designer
designer is a visual way of building
machine learning models
with a drag and drop features and
machine learning has also a lot of
features
around asset management assets are
things like data sets
experiments pipelines models that you've
built
and endpoints that you deploy those
models to there's also plenty of other
features which allow you to manage your
compute resources where you train and
deploy your models
data stores where you connect to your
data sources in azure for example azure
blob storage and azure file share
but for now let me quickly go back to
designer where i will build my own
machine learning model using a visual
experience
and before i begin i will create a
compute target so a simple virtual
machine that will run my workflow
so let me simply hit create new and use
predefined size
give it a name like my compute
and hit save after the machine has been
provisioned
i can simply select it hit save and
start building my workflow
i will speed this part up because for
the fundamentals training the process
itself is not important but i want to
show you how easy it is
simply drag and drop your data then
select the cons that will be used
to build machine learning model then
clean your data using another step
and split your data so that you can grab
part of the data for the training and
part for the evaluation of your model
next simply train your model to forecast
the price using linear regression
if you're happy with it simply drag and
drop another block to score the model
and the last step to evaluate the
results and we're done
if we are happy with our pipeline we can
submit it to run
by either choosing existing or new
experiment
let's create new one let's give it a
name
i will call my experiment demo and
submit it to run
depending on the complexity of your
pipeline and the machine that you pick
to train the model
this process might take from few minutes
to even half an hour or even
couple of hours so i've sped this up by
a lot
just to show you that ui very nicely
shows you how your model is being built
in real time and now if you are a data
scientist you can either check the
evaluation results so you can check all
the logs all the outputs that we're
generating during this pipeline check
all the details
or by going to score model where they
can visualize their data sets
where they can see a lot of extra
information about the data set they use
for
training of this model including the
price that they forecasted
and the scores so to summarize azure
machine learning service
is an end-to-end cloud-based platform
for creating managing and publishing
of our machine learning models this is
of course a platform as a service
offering in azure
a top level resource in azure machine
learning service
is called machine learning workspace
think of it like this workspace ties
everything together all the computer
resources all the permissions
all the runs pipelines experiments
history
connection to external services like
azure storage accounts container
registry deployments of your model
literally everything is managed through
azure machine learning
workspace and machine learning studio is
this web portal that we were using for
our end-to-end management of the
workspace
a quick note that i have for you here is
that in the past there was another
service called
machine learning studio this is
completely different from the one that
we're talking about today
because that service is no longer
actively being developed and new
customers are being encouraged to use
new machine learning because it also has
the studio experience
with some additional features and the
key features that you get by using azure
machine learning service are of course
notebooks
written in python or r automated ml
where you can throw
a lot of algorithms and tweak some
parameters to find the best algorithm
and to build the best model for your
data
and with visual designer you can build
your machine learning pipelines without
writing a single line of code
you can also manage your data and
compute resources
so all the resources to manage and store
your data
as well as process those models and
deploy them as a web services
and lastly everything is nicely tied
into other machine learning pipelines
which allow you to orchestrate this
model training deployment and all the
management tasks
as always all the materials for this
episode are found
on my website under episode 16. and
that's it for this one but don't go away
because next episode is all about
serverless computing in azure
so if you like my work support the
channel by subscribing liking and
commenting and
see in the next one
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