Introduction to MLOps and Vertex Pipelines

Want to make sure your data science project makes it into production? In this episode of AI Simplified, we’ll learn how to operationalize your workflow with Vertex Pipelines, while ensuring each step is scalable and reproducible. Watch to learn how MLOps can unify ML system development!

Chapters:
0:00 - Intro & Why MLOps?
1:30 - What is MLOps?
1:40 - High-level MLOps framework
5:13 - What are Vertex Pipelines?
6:15 - Why use Vertex Pipelines if you already use Kubeflow?
7:40 - Summary

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#AISimplified

product: Cloud - General; fullname: Priyanka Vergadia; re_ty: Publish;

[stanwest]

Excellent job Priyanka. Went through all eight Vertex videos and found them very engaging and informative. Your enthusiasm is infectious. Are you actually writing backwards, or being left handers helps - it looks magical. Very artist like writing. When showing your screen can you try using a more prominent icon for the mouse pointer. I have seen videos with a yellow circle which is easier to follow. Thanks.

[savchenkooleksandr]

Many times I came to the conclusion that instructions for various Google services have a rather poor description: 1. information is often unstructured 2. too many details that lead away from the main line 3. the same things are named differently. 4. there is no 'hello, world' stage, followed by deepening into details. This series of videos surprised me. Perhaps watching a video is much better than reading instructions. Google should take a cue from this lecturer! 👍👋

[Khaled.Jallad]

Think you that was a helpful video on how to implement the workflow on mlops

[TheRedValue]

I'm impressed at your ability to draw and write backwards :O

[itsvike]

very elaborative and brilliant presentation as always!

[Love_and_wisdom]

Thanks for the video! Getting started with GCP !

[simonmitnick]

each step runs in a reproducible, auditable, cost-effective and a scalable way 💯

[fwmevch]

I have successfully trained a model and can fetch predictions from an endpoint. However, I'm encountering an error when attempting to use the model in the following code:

python
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model =
The error message I'm receiving is:

vbnet
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NotFound: 404 Publisher Model is not found.
Could you please provide guidance on how to correctly use my trained model in this code?

Additionally, I'm interested in querying my CSV file using this model. Could you please provide a solution for this as well?

[mwdcodeninja]

How long did it take to learn to write mirrored? Great talk!

[flyingtiger]

Great Video and content, not to demean but Videos will be better off without any human visuals, only content+audio is sufficient.

[bk]

Sorry for a stupid question, but how 9/10 of projects came to 87%, but not to 90%?

[nicholasroth]

I disagree with the implication at the start of the video that most ML models fail to launch due to engineering issues. In my experience, it's always been that the stakeholders don't need the model anymore or that there's not enough signal in the data for a model to predict. The impact from those common situations can be mitigated by building a PoC and failing early if the effort is going to fail, validating the product and need before building the big production ML pipeline.