Name: Intro to Kubeflow Pipelines
Uploaded: 2021-06-15T16:25:56.000Z
Duration: 4 min 21 s
Description: VoiceTubeの動画で発音を聞きながら英語表現を覚えよう！学べる英語：

can involve many steps, from data prep to training

It's hard to track these in an ad hoc manner,

On top of that, monitoring and version tracking

Kubeflow Pipelines lets data scientists codify their ML

workflows so that they're easily composable, shareable,

Let's check out how it can help you achieve

If you want to check out the documentation directly,

check out the link below to read more about Kubeflow Pipelines.

Kubeflow Pipelines help drive data scientists

to adopt a disciplined pipeline mindset when developing ML code

like simplifying the orchestration of machine

learning pipelines, making experimentation easy

for you to try ideas, reproduce runs, and share pipelines.

And you can stitch together and reuse components and pipelines

without having to rebuild every time, like building blocks.

for things like execution monitoring, workflow

scheduling, metadata logging, and versioning.

Kubeflow Pipelines is one of the Kubeflow core components.

It's automatically deployed during Kubeflow deployment.

Now, there are many ways of defining pipelines

But for Kubeflow, a pipeline is a description

which provide portability, repeatability,

and encapsulation, because it decouples the execution

includes all the components in the workflow

and how they combine, and that makes a graph.

It includes the definition of all the inputs or parameters

A pipeline component is one step in the workflow that

does a specific task, which means it takes inputs and can

An output of a component can become the input

Think of it like a function, in that it has a name, parameters,

For example, a component can be responsible for data

preprocessing, data transformation, model training,

Now, this is where Kubeflow Pipelines shines.

A pipeline component is made up of code, packaged

The system launches one or more Kubernetes pods corresponding

You can also leverage prebuilt components found

Under the hood, the pods start docker containers,

Containers can only give you composability and scalability,

you can also work with pipelines via the Kubeflow UI.

That way you can leverage some of its powerful features,

like visualizing the pipeline through a graph.

As you execute a run, the graph shows the relationships

Once a step completes, you'll also see the output artifact

As you can see here, the UI takes the output

and can actually render it as a rich visualization.

You can also get statistics on the performance

And we can't forget about the experiments in the UI,

which let you group a few of your pipeline runs

You can compare the results between experiments

Because they're built on the flexibility of the containers,

Kubeflow Pipelines are useful for all sorts of tasks,

like ETL and CI/CD, but they're most popularly used

While you can deploy it on your own installation of Kubeflow,

a new hosted version of Pipelines on Google Cloud's AI

platform lets you deploy a standalone version

of Kubeflow Pipelines on a GKE cluster in just a few clicks.

字幕リスト動画再生

Intro to Kubeflow Pipelines

discipline

mindset

aspect

scale