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...
Hi,I can train, registered a ML Model in my Datbricks Workspace.Then, to deploy it on AKS, I need to register the model in Azure ML, and then, deploy to AKS.Is it possible to skip the Azure ML step?I would like to deploy directly into my AKS instance...
Is it still the case, can't we serve the model in Databricks. I am new to this, so I am just wondering the capabilities.
For my RAG use case, I've registered my langchain chain as a model to Unity Catalog. When I'm trying to serve the model, container image creation fails with the following error in the build log:[...] #16 178.1 Downloading langchain_core-0.3.17-py3-no...
I was able to solve the problem by adding python-snappy==0.7.3 to the requirements.
Hi, I've got a question about deploying an endpoint for Llama 3.1 8b. The following code should create the endpoint without scale-to-zero. The endpoint is being created, but with scale-to-zero, although scale_to_zero_enabled is set to False. Instead ...
Thanks for the reply @Walter_C. This didn't quite work, since it used a CPU and didn't consider the max_provisioned_throughput, but I finally got it to work like this: from mlflow.deployments import get_deploy_client client = get_deploy_client("data...
Hi I have created a model and pipeline using xgboost.spark's sparkXGBregressor and pyspark.ml's Pipeline instance. However, i run into a "RuntimeError: _get_spark_session should not be invoked from executor side." when i try to save the predictions i...
The error you're encountering is due to attempting to access the Spark session on the executor side, which is not allowed in Spark's distributed computing model. This typically happens when trying to use Spark-specific functionality within a UDF or d...
I'm trying to delete rows from a table with the same date or id as records in another table. I'm using the below query and get the error 'Multi-column In predicates are not supported in the DELETE condition'. delete from cost_model.cm_dispatch_consol...
I seem to get this error on some DeltaTables and not others:df.createOrReplaceTempView("channels_to_delete") spark.sql(""" delete from lake.something.earnings where TenantId = :tenantId and ChannelId = in ( select ChannelId ...
Hi EveryoneI tried to follow the same steps in Topic from Text on similar data as example. However, when I tri to fit the model with data I get this error.IllegalArgumentException: requirement failed: Column features must be of type equal to one of t...
Hi @amirA ,The LDA model expects the features column to be of type Vector from the pyspark.ml.linalg module, specifically either a SparseVector or DenseVector, whereas you have provided Row type.You need to convert your Row object to SparseVector.Che...
Hello, I trained a model using MLFlow, and saved the model as an artifact. I can load the model from a notebook and it works as expected (i.e. I can load the model using its URI).However, when I want to deploy it using Databricks endpoints, container...
@ivan_calvo The problem still exists. Surely there has to be some other option than downgrading the ML cluster to DBR 14.3 LTS ML?
I want to develop an automated lead allocation system to prospect sales representatives. Please suggest a suitable solution also any links if available.
Hi jamesl,My use case is related to match the prospect sales agent for the customer entering retail store, when a customer enters a store based on the inputs provided and checking on if the customer is existing or new customer, I want to create a rea...
There could be multiple reasone why you're getting this error @avishkarborkar . If the course you're following requires Unity Catalog, first you need to check if you have a premium workspace. Next you need to make sure that your workspace is enabled ...
https://github.com/mlflow/mlflow/issues/11077 In Databricks, when attempting to set an experiment with an experiment_name specified as an absolute path from /Workspace/Shared/mlflow_experiment/<experiment_name>, the mlflow.set_experiment() function ...
Before setting the experiment, use mlflow.get_experiment_by_name() to check if the experiment already exists. If it does, you can set the experiment without attempting to create it again.
I am building and MLOps architecture.I do not want to deploy the training workflow to prod. My first approach was to selectively not deploy the workflow to prod, but this does not seem to be possible as in this thread:https://community.databricks.com...
Target Override Feature: You can use the target override feature to specify different configurations for different environments. However, this does not provide a direct way to exclude specific job resources.Environment-Specific Folders: Another app...
Hi all,I have been using databricks-connect with serverless compute to develop and debug my databricks related code. It worked great so far. Now I started integrating ML-Flow in my workflow, and I am encountering an issue. When I run the following co...
The error you are encountering, pyspark.errors.exceptions.connect.AnalysisException: [CONFIG_NOT_AVAILABLE] Configuration spark.mlflow.modelRegistryUri is not available. SQLSTATE: 42K0I, is a known issue when using MLflow with serverless clusters in ...
Hi , I've completed the course Machine Learning with Databricks ! Looking forward to learn more .
Hi Databricks support,I'm training an ML model using mlflow on DBR 13.3 LTS ML, Spark 3.4.1 using databricks.automl_runtime 0.2.17 and databricks.automl 1.20.3, with shap 0.45.1. My training data has two float-type columns with three or fewer unique ...
Hi @rtreves , sorry I was not able to investigate on the above. Not sure if you would be able to create a support ticket with Databricks as it may be an involved effort to review the code. I do have a suggestion, instead of relying on the automatic ...
