u/ptab0211

Hey guys, i was curios, what is the usual setup when having deploy code pattern for model training, so idea is that data scientist run model experiments, different featurization, and just iterate fast on the data on development workspace/environment. Each developer gets its own schema for isolation.

Then when they got something which they want to be promoted, what happens? Of course output of this stage is the training pipeline code, but for example, they did the full hyper-parameter tuning experimentation, so with actual training pipeline code which goes through code quality checks, unit testing, type hinting, do we promote:

a) same hyper-parameters tuning search space (what about cost, variance of possible options etc..)

b) narrowed down search space for tuning

c) parameters of best fitted model

Also do we write this into yaml files within the repo, or there is some better practices where u just fetch ml experiment metadata, or write to UC Volumes, generally interested to see what people are using for this.

Thanks

Hey guys, i was curios, what is the usual setup when having deploy code pattern for model training, so idea is that data scientist run model experiments, different featurization, and just iterate fast on the data on development workspace/environment. Each developer gets its own schema for isolation.

Then when they got something which they want to be promoted, what happens? Of course output of this stage is the training pipeline code, but for example, they did the full hyper-parameter tuning experimentation, so with actual training pipeline code which goes through code quality checks, unit testing, type hinting, do we promote:

a) same hyper-parameters tuning search space (what about cost, variance of possible options etc..)

b) narrowed down search space for tuning

c) parameters of best fitted model

Also do we write this into yaml files within the repo, or there is some better practices where u just fetch ml experiment metadata, or write to UC Volumes, generally interested to see what people are using for this.

Thanks

Hey guys, so i am arriving after midnight on airport, what are my options to get to the apartment? I am staying in Lefka apartments/hotel, i see its around 30 minutes by car from airport, how much would taxi cost me from the airport?

Hey, how do people use the serving_endpoint resource in Databricks Asset Bundles?

For example, i have a model_training job that produces a new model version, which kicks off a model_deployment job that validates the new version against the current `@Champion`. If validation passes, we promote the alias and gradually roll out traffic on the real-time endpoint, ramping to 100% if it stays healthy.

For the gradual rollout, the deployment job calls `update_endpoint` via the Python SDK to shift traffic between served_entities. The moment that runs, the endpoint drifts from whatever `entity_version` is pinned in the YAML — and any future `bundle deploy` would revert serving back to the old version.

So what is the point of the serving_endpoint resource in DABs if i need to update it via SDK anyway?

Hey, how do people use the serving_endpoint resource in Databricks Asset Bundles?

For example, i have a model_training job that produces a new model version, which kicks off a model_deployment job that validates the new version against the current `@Champion`. If validation passes, we promote the alias and gradually roll out traffic on the real-time endpoint, ramping to 100% if it stays healthy.

For the gradual rollout, the deployment job calls `update_endpoint` via the Python SDK to shift traffic between served_entities. The moment that runs, the endpoint drifts from whatever `entity_version` is pinned in the YAML — and any future `bundle deploy` would revert serving back to the old version.

So what is the point of the serving_endpoint resource in DABs if i need to update it via SDK anyway?

Hey, what is the standard for tracking which model produced which row prediction, for example, i have inference batch table where i just append results and share with clients, and models are being retrained constantly and new `@Champion` is always being promoted.

Do i just append model_version, run_id and some additional metadata so i can just manually have full lineage or there is some more out of box solution by Databricks?

Hey, what is the standard for tracking which model produced which row prediction, for example, i have inference batch table where i just append results and share with clients, and models are being retrained constantly and new `@Champion` is always being promoted.

Do i just append model_version, run_id and some additional metadata so i can just manually have full lineage or there is some more out of box solution by Databricks?

Hi guys, how does your model training pipeline (train - validate - promote) on Databricks look like?

Basically idea is to use deploy code pattern, where e.g. u have access on dev to prod data, so u can experiment with different models, different parameters, hyper param tuning etc... so classic model development cycle, once u are confident in your model performance on the dev, you need to manually take out your best training parameters from experiment, put it into some human readable code (yaml file), deploy code pipeline to staging, run some testing that nothing breaks, then in production, with that best parameters, you do model training pipeline again where u possibly challenge the model which runs in production.

Is this standard? I am wondering that this way u are never sure that u will reproduce what u have got on dev while experimenting on the production. How do u promote your models? How do u train your models?

Hey everyone, how does your model training pipeline (train - validate - promote) on Databricks look like? Basically idea is to use deploy code pattern, where e.g. u have access on dev to prod data, so u can experiment with different models, different parameters, hyper param tuning etc... so classic model development cycle, once u are confident in your model performance on the dev, you need to manually take out your best training parameters from experiment, put it into some human readable code (yaml file), deploy code pipeline to staging, run some testing that nothing breaks, then in production, with that best parameters, you do model training pipeline again where u possibly challenge the model which runs in production.

Is this standard? I am wondering that this way u are never sure that u will reproduce what u have got on dev while experimenting on the production. How do u promote your models? How do u train your models?

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Thanks =)

Hi, i have never seen a positive comment about this product from Databricks? Do teams really use this ever? Do they use FE client to create tables, training set, for online inference, etc...?

All i see is complaints, i never see what is wrong with this tool/product, can someone share their experience?