- 459 Views
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Resolved! Difference between Workspace and Unity Catalog experiments when using MLflow autologging?
Hi everyone,I am trying to understand the exact differences between using Workspace experiments versus Unity Catalog experiments, specifically in the context of MLflow autologging (mlflow.autolog()).Does autologging behave differently depending on wh...
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Great question! I’d also be interested to know if autologging has any behavioral or permission differences with Unity Catalog experiments, especially around governance and model lineage.
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- 1767 Views
- 2 replies
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Learn Databricks AI medium article series for fellow learners.
When it comes to machine learning, the platform plays a pivotal role in successful implementation. Databricks offers a best-in-class machine learning platform with cutting-edge features such as MLflow, Model Registry, Feature Store, and MLOps, which ...
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Great share! Databricks really has built something special on the ML side — MLflow alone has become almost a default for experiment tracking, and pairing it with Model Registry and Feature Store makes the whole path from experimentation to productio...
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- 561 Views
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End-to-End Streaming NLP Pipeline with GDELT, Azure Data Factory, ADLS Gen2 and Databricks
I have been working on a project to understand Databricks end to end, rather than just loading some data and training a model.I picked GDELT news data and the use case is to identify supply chain disruption related news and eventually predict which e...
- 561 Views
- 4 replies
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@kartheek_rao, you are on the right track.Since you are doing clustering of new articles, it's unsupervised learning; you need to understand the feature engineering part more and the EDA part with MLFlow experiments. You have model monitoring as well...
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- 1406 Views
- 1 replies
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Resolved! Can Databricks Jobs Run on Kubernetes Clusters?
Context: We're exploring using Kubernetes (EKS) as our compute infrastructure instead of Databricks managed clusters. We want to understand if Databricks can orchestrate, deploy, and monitor jobs that run on a Kubernetes cluster.Questions:Is it possi...
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Hi @ASH1243434 ,Unfortunately, the Databricks cannot natively route job execution into your EKS cluster. There is no "external compute" or "bring your own Kubernetes" option in Databricks Jobs configuration. If my answer was helpful, please consider ...
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- 2698 Views
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Resolved! Which types of model serving endpoints have health metrics available?
I am retrieving a list of model serving endpoints for my workspace via this API: https://docs.databricks.com/api/workspace/servingendpoints/listAnd then going to retrieve health metrics for each one with: https://[DATABRICKS_HOST]/api/2.0/serving-end...
- 2698 Views
- 2 replies
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Your observation is correct—this behavior is expected.Endpoints with entity_type = FOUNDATION_MODEL_API do not expose health metrics via the /metrics endpoint, which is why you’re getting 404 responses. These endpoints are fully managed, multi-tenant...
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- 3007 Views
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Resolved! Generic Spark Connect ML error. The fitted or loaded model size is too big.
When I train models in the serverless environment V4 (Premium Plan), the system occasionally returns the error message listed below, especially after running the model training code multiple times. We have tried creating new serverless sessions, whic...
- 3007 Views
- 4 replies
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Hi @jayshan, I'm sorry for the delayed response to your question. And, thanks for the extra details and for sharing your workaround. This behaviour is tied to how Spark Connect ML works in serverless mode, rather than a traditional JVM/GC leak. On se...
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- 3558 Views
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Resolved! Is Delta Lake deeply tested in Professional Data Engineer Exam?
I wanted to ask people who have already taken the Databricks Certified Professional Data Engineer exam whether Delta Lake is tested in depth or not. While preparing, I’m currently using the Databricks Certified Professional Data Engineer sample quest...
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Yes, Delta Lake concepts are an important part of the Databricks Professional Data Engineer exam, but they aren’t tested in extreme depth compared to core Spark transformations and data pipeline design. The exam mainly focuses on practical understand...
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- 915 Views
- 2 replies
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Getting error when running databricks deploy bundle command
HI all,I am trying to implement MLOps project using https://github.com/databricks/mlops-stacks repo.I have created azure databricks with Premium (+ Role-based access controls) (Click to change) and following bundle creation and deploy using uRL: http...
- 915 Views
- 2 replies
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This is expected behavior with mlops-stacks and not an issue with your Terraform version or the CLI. The main problem is that your Azure Databricks workspace does not have Unity Catalog enabled or assigned. The mlops-stacks templates assume Unity Cat...
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- 1393 Views
- 1 replies
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AutoML Deprecation?
Hi All,It looks like AutoML is set to be deprecated with the next major version (although the note isn't specific on if that's 18). I haven't seen any announcement or alert about this impending change. Did I just miss it? I know we have teams using t...
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Hi @kevin11 ,I guess it's their standard way of library deprecation policy. In their docs they mentioned that when a library is planned for removal, Databricks takes following steps to notify customers:So they've added those note to AutoMl docs:And y...
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- 5087 Views
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Custom AutoML pipeline: Beyond StandardScaler().
The automated notebook pipeline in an AutoML experiment applies StandardScaler to all numerical features in the training dataset as part of the PreProcessor. See below.But I want a more nuanced and varied treatment of my numeric values (e.g. I have l...
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Greetings @sharpbetty Great question! Databricks AutoML's "glass box" approach actually gives you several options to customize preprocessing beyond the default StandardScaler. Here are two practical approaches: Option A: Pre-process Features Before ...
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- 5953 Views
- 2 replies
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Resolved! AutoML master notebook failing
I have recently been able to run AutoML successfully on a certain dataset. But it has just failed on a second dataset of similar construction, before being able to produce any machine learning training runs or output. The Experiments page says```Mo...
- 5953 Views
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Hi @dkxxx-rc , Thanks for the detailed context. This error is almost certainly coming from AutoML’s internal handling of imbalanced data and sampling, not your dataset itself. The internal column _automl_sample_weight_0000 is created by AutoML when i...
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- 4506 Views
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Patient Risk Score based on health history: Unable to create data folder for artifacts in S3 bucket
Hi All,we're using the below git project to build PoC on the concept of "Patient-Level Risk Scoring Based on Condition History": https://github.com/databricks-industry-solutions/hls-patient-riskI was able to import the solution into Databricks and ru...
- 4506 Views
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Greetings @SreeRam , here are some suggestions for you. Based on the error you're encountering with the hls-patient-risk solution accelerator, this is a common issue related to MLflow artifact access and storage configuration in Databricks. The probl...
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- 5052 Views
- 1 replies
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AutoML "need to sample" not working as expected
tl; dr:When the AutoML run realizes it needs to do sampling because the driver / worker node memory is not enough to load / process the entire dataset, it fails. A sample weight column is NOT provided by me, but I believe somewhere in the process the...
- 5052 Views
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Hey @sangramraje , sorry for the late response. I wanted to check in to see if this is still an issue with the latest release? Please let me know. Cheers, Louis.
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- 1283 Views
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Resolved! How does Databricks AutoML handle null imputation for categorical features by default?
Hi everyone I’m using Databricks AutoML (classification workflow) on Databricks Runtime 10.4 LTS ML+, and I’d like to clarify how missing (null) values are handled for categorical (string) columns by default.From the AutoML documentation, I see that:...
- 1283 Views
- 1 replies
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Hello @spearitchmeta , I looked internally to see if I could help with this and I found some information that will shed light on your question. Here’s how missing (null) values in categorical (string) columns are handled in Databricks AutoML on Dat...
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- 4427 Views
- 3 replies
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Spark context not implemented Error when using Databricks connect
I am developing an application using databricks connect and when I try to use VectorAssembler I get the Error sc is not none Assertion Error. is there a workaround for this ?
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Ran into exactly the same issue as @Łukasz1 After some googling, I found this SO post explaining the issue: later versions of databricks connect no longer support the SparkContext API. Our code is failing because the underlying library is trying to f...
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