Add "From Raw Data to Model Serving: A Blueprint for the AI/ML Lifecycle with Kubeflow" blog post #170
Conversation
…cle with Kubeflow" blog post Signed-off-by: Helber Belmiro <[email protected]>
hbelmiro
force-pushed
the
fraud-detection-e2e
branch
from
86c034c to
1852f23
Compare
|
@franciscojavierarceo @tarilabs @anishasthana can you guys PTAL? |
google-oss-prow
bot
requested review from
franciscojavierarceo and
juliusvonkohout
|
|
||
| By following this blueprint, you can adapt and extend the process for your own machine learning projects, whether you're working locally or scaling up to production. Kubeflow's modular platform and ecosystem make it possible to manage the entire ML lifecycle in a consistent, automated, and open way. | ||
|
|
||
| Ready to try it yourself? The complete source code for this project is available on [GitHub](https://github.com/hbelmiro/fraud_detection_e2e_demo/tree/kubeflow). |
There was a problem hiding this comment.
in an ideal world we'd put this in the example repos but we've stated that the examples repo is not maintained.
Signed-off-by: Helber Belmiro <[email protected]>
There was a problem hiding this comment.
I won't have time to run through the demo right now -- @hbelmiro has anyone else run through the entire process e2e? Just to ensure there wasn't some configuration in your system that you weren't aware of.
|
|
||
| ### Pushing Images | ||
|
|
||
| After building, push the images to a container registry accessible by your Kubernetes cluster. Update the image references in your pipeline or manifests as needed. |
There was a problem hiding this comment.
Should we include a link or pointers at what manifests would likely need updates?
There was a problem hiding this comment.
Actually, only the pipeline needs to be updated. I removed or manifests from the sentence.
|
|
||
| ### 2. Feature Engineering with Feast | ||
|
|
||
| [Feast](https://feast.dev/) is an open source feature store that lets you manage and serve features for both training and inference, ensuring consistency and reducing the risk of training/serving skew. |
There was a problem hiding this comment.
Should we also include a definition of "feature" in a sentence or two here?
|
|
||
| 1. After uploading, click on your pipeline in the list. | ||
| 2. Click **Create run**. | ||
| 3. Fill in the run details and click **Start**. |
There was a problem hiding this comment.
We can just trust the defaults or optionally customize the run name and description.
I rephrased.
@anishasthana I'm not aware of anyone who did it. |
Co-authored-by: Anish Asthana <[email protected]> Signed-off-by: Helber Belmiro <[email protected]>
Co-authored-by: Anish Asthana <[email protected]> Signed-off-by: Helber Belmiro <[email protected]>
Co-authored-by: Anish Asthana <[email protected]> Signed-off-by: Helber Belmiro <[email protected]>
Signed-off-by: Helber Belmiro <[email protected]>
Signed-off-by: Helber Belmiro <[email protected]>
Signed-off-by: Helber Belmiro <[email protected]>
Signed-off-by: Helber Belmiro <[email protected]>
|
This is huge @hbelmiro!! Thank you for it!! CC @andreyvelich @vikas-saxena02 /lgtm |
|
[APPROVALNOTIFIER] This PR is APPROVED This pull-request has been approved by: franciscojavierarceo The full list of commands accepted by this bot can be found here. The pull request process is described here
Needs approval from an approver in each of these files:
Approvers can indicate their approval by writing |
Resolves #171