Development process

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来自 Google Cloud 的课程

How Google does Machine Learning

543 个评分

Google Cloud

How Google does Machine Learning

543 个评分

课程 1（共 5 门，Specialization Machine Learning with TensorFlow on Google Cloud Platform）

What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently -- of being about logic, rather than just data. We talk about why such a framing is useful when thinking about building a pipeline of machine learning models. Then, we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important the phases not be skipped. We end with a recognition of the biases that machine learning can amplify and how to recognize this.

从本节课中

Python notebooks in the cloud

This module covers Cloud Datalab, which is the development environment you will use in this specialization.

Cloud Datalab1:21

Demo: Cloud Datalab1:16

Development process2:56

Demo of rehosting Cloud Datalab3:12

与讲师见面

Google Cloud Training

Have you used Google Docs?

How is it different from documents edited in a desktop editor?

Have you filed taxes online?

How is the experience different from doing your taxes in a desktop program?

There are lots of benefits, but one key aspect is collaboration.

You don't have to email documents back and forth.

You can work on your taxes, alongside your accountant.

When I first started doing scientific research,

collaborating n a single result was painful.

I'd write some code and create a graph and then I would create a snapshot, create

the image file, put it into a doc, create a PDF and send it to my collaborator.

And a few hours later, my colleague would say, looks great, but

could you add one more year of data?

It looks kind of sparse.

And I'd go through the process all over again, why?

Because the PDF I'd sent along was not editable.

Round trips took a long time.

Enter Python notebooks.

I'd write the code, create the graph, write some commentary and

send the notebook link to my colleague.

And when my colleague wanted to add one more year of data, she would simply edit

the cell, look at the new graph and then say see, looks a lot better.

And that was great.

We now had a better notebook to do the next step.

But one problem with traditional notebooks,

who runs the server that hosts these pages?

Whose machine?

If it is mine and my machine goes to sleep, then my colleague can't work.

When your Datalab notebook is hosted in the cloud,

you can develop together quite easily.

And just as Google Docs are available, even when your computer isn't on.

So too are Datalab notebooks, when you run them on the cloud.

To share a notebook within a project,

other users can simply Datalab connect to the VM and work using the url.

Another way to share notebooks is through revision control systems such as Git.

Cloud also frees you from being constrained by hardware limitations.

Not only can you run Datalab in any computer instance you want,

you can also change the machine specs after it's been provisioned.

You can go to the web console, find the running VM, stop it and

restart it with a new machine configuration.

As a developer,

to work in Datalab, you simply connect the VM that's running the notebook server.

The notebooks themselves can be persisted in Git, so

you can delete the VM if you don't need it anymore.

And when you run the notebook, the computation is carried out on the VM.

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