Introduction to MLOps and Vertex Pipelines

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  • čas přidán 8. 09. 2024

Komentáře • 27

  • @TheRedValue
    @TheRedValue Před 3 lety +6

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

    • @TheRedValue
      @TheRedValue Před 3 lety +1

      Also: I just released my big AI project, which means it is one of those 1 out of 10 that did make it into production :D

    • @infiniteloop5449
      @infiniteloop5449 Před rokem

      its crazy mad skills, who can write in mirror image form!

  • @stanwest8103
    @stanwest8103 Před 3 lety +6

    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.

  • @savchenkooleksandr2191
    @savchenkooleksandr2191 Před rokem +2

    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
    @Khaled.Jallad Před rokem

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

  • @Love_and_wisdom
    @Love_and_wisdom Před 3 lety +2

    Thanks for the video! Getting started with GCP !

  • @itsvike
    @itsvike Před 3 lety +3

    very elaborative and brilliant presentation as always!

  • @simonmitnick3339
    @simonmitnick3339 Před 2 lety +1

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

  • @mohanvoleti
    @mohanvoleti Před 2 lety +1

    Super explanation

  • @aslihanasadov4714
    @aslihanasadov4714 Před 2 lety

    Brilliant and engaging presentation!

  • @dheer211
    @dheer211 Před 3 lety

    Very good introduction to MLOps

  • @mwdcodeninja
    @mwdcodeninja Před 3 lety +1

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

    • @254gahemd
      @254gahemd Před 3 lety +2

      Not sure how committed google is. But I expect she is wearing a mirrored logo shirt, writing normally and then the video is mirrored in post. Hence (possibly) we see ring on the right hand and she is left handed.

  • @user-wr4yl7tx3w
    @user-wr4yl7tx3w Před rokem

    Cool!

  • @flyingtiger741
    @flyingtiger741 Před rokem

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

  • @user-fw6me2vc8h
    @user-fw6me2vc8h Před 11 měsíci

    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
    Copy code
    model = TextGenerationModel.from_pretrained("*********")
    The error message I'm receiving is:
    vbnet
    Copy code
    NotFound: 404 Publisher Model `publishers/google/models/********` 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?

  • @connierharrison8336
    @connierharrison8336 Před rokem

    Turn on sound please

  • @sanjumanna5007
    @sanjumanna5007 Před 3 lety

    ❤️

  • @bk3460
    @bk3460 Před 2 lety

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

  • @HansHjelm
    @HansHjelm Před 2 lety +1

    Wouldn't that be more like 1/8?

  • @nicholasroth1608
    @nicholasroth1608 Před rokem +1

    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.

    • @jean4j_
      @jean4j_ Před rokem

      Agreed! 100%
      If it's decided that the model does bring a good business value, it's definitely do-able to re-write the algorithm (with the help of software engineers if needed) to have a proper ML pipeline.
      It's more a business problem than a software problem in my view.
      But sure it's always better to produce a quality software from the start