Module not found errors such as,

No module named 'sklearn.decomposition._truncated_svd'

Import errors such as,

ImportError: cannot import name 'RollingOriginValidator' ,

Attribute errors such as,

AttributeError: 'SimpleImputer' object has no attribute 'add_indicator'

Resolutions depend on your AutoML SDK training version:

If your AutoML SDK training version is greater than 1.13.0, you need pandas == 0.25.1 and scikit-learn==0.22.1 .

If there is a version mismatch, upgrade scikit-learn and/or pandas to correct version with the following,

    pip install --upgrade pandas==0.25.1
    pip install --upgrade scikit-learn==0.22.1
If your AutoML SDK training version is less than or equal to 1.12.0, you need pandas == 0.23.4 and sckit-learn==0.20.3.
If there is a version mismatch, downgrade scikit-learn and/or pandas to correct version with the following,
  pip install --upgrade pandas==0.23.4
  pip install --upgrade scikit-learn==0.20.3
Setup
AutoML package changes since version 1.0.76 require the previous version to be uninstalled before updating to the new version.
ImportError: cannot import name AutoMLConfig
If you encounter this error after upgrading from an SDK version before v1.0.76 to v1.0.76 or later, resolve the error by running: pip uninstall azureml-train automl and then pip install azureml-train-automl. The automl_setup.cmd script does this automatically.
automl_setup fails
On Windows, run automl_setup from an Anaconda Prompt. Install Miniconda.
Ensure that conda 64-bit version  4.4.10 or later is installed. You can check the bit with the conda info command. The platform should be win-64 for Windows or osx-64 for Mac. To check the version use the command conda -V. If you have a previous version installed, you can update it by using the command: conda update conda. To check  32-bit by running
Ensure that conda  is installed.
Linux - gcc: error trying to exec 'cc1plus'
If the gcc: error trying to exec 'cc1plus': execvp: No such file or directory error is encountered, install the GCC build tools for your Linux distribution. For example, on Ubuntu, use the command sudo apt-get install build-essential.
Pass a new name as the first parameter to automl_setup to create a new conda environment. View existing conda environments using conda env list and remove them with conda env remove -n <environmentname>.
automl_setup_linux.sh fails: If automl_setup_linus.sh fails on Ubuntu Linux with the error: unable to execute 'gcc': No such file or directory
Make sure that outbound ports 53 and 80 are enabled. On an Azure virtual machine, you can do this from the Azure portal by selecting the VM and clicking on Networking.
Run the command: sudo apt-get update
Run the command: sudo apt-get install build-essential --fix-missing
Run automl_setup_linux.sh again
configuration.ipynb fails:
For local conda, first ensure that automl_setup has successfully run.
Ensure that the subscription_id is correct. Find the subscription_id in the Azure portal by selecting All Service and then Subscriptions. The characters "<" and ">" should not be included in the subscription_id value. For example, subscription_id = "12345678-90ab-1234-5678-1234567890abcd" has the valid format.
Ensure Contributor or Owner access to the subscription.
Check that the region is one of the supported regions: eastus2, eastus, westcentralus, southeastasia, westeurope, australiaeast, westus2, southcentralus.
Ensure access to the region using the Azure portal.
workspace.from_config fails:
If the call ws = Workspace.from_config() fails:
Ensure that the configuration.ipynb notebook has run successfully.
If the notebook is being run from a folder that is not under the folder where the configuration.ipynb was run, copy the folder aml_config and the file config.json that it contains to the new folder. Workspace.from_config reads the config.json for the notebook folder or its parent folder.
If a new subscription, resource group, workspace, or region, is being used, make sure that you run the configuration.ipynb notebook again. Changing config.json directly will only work if the workspace already exists in the specified resource group under the specified subscription.
If you want to change the region, change the workspace, resource group, or subscription. Workspace.create will not create or update a workspace if it already exists, even if the region specified is different.
TensorFlow
As of version 1.5.0 of the SDK, automated machine learning does not install TensorFlow models by default. To install TensorFlow and use it with your automated ML experiments, install tensorflow==1.12.0 via CondaDependencies.
  from azureml.core.runconfig import RunConfiguration
  from azureml.core.conda_dependencies import CondaDependencies
  run_config = RunConfiguration()
  run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['tensorflow==1.12.0'])
Numpy failures
import numpy fails in Windows: Some Windows environments see an error loading numpy with the latest Python version 3.6.8. If you see this issue, try with Python version 3.6.7.
import numpy fails: Check the TensorFlow version in the automated ml conda environment. Supported versions are < 1.13. Uninstall TensorFlow from the environment if version is >= 1.13.
You can check the version of TensorFlow and uninstall as follows:
Start a command shell, activate conda environment where automated ml packages are installed.
Enter pip freeze and look for tensorflow, if found, the version listed should be < 1.13
If the listed version is not a supported version, pip uninstall tensorflow in the command shell and enter y for confirmation.
jwt.exceptions.DecodeError
Exact error message: jwt.exceptions.DecodeError: It is required that you pass in a value for the "algorithms" argument when calling decode().
For SDK versions <= 1.17.0, installation might result in an unsupported version of PyJWT. Check that the PyJWT version in the automated ml conda environment is a supported version. That is PyJWT version < 2.0.0.
You may check the version of PyJWT as follows:
Start a command shell and activate conda environment where automated ML packages are installed.
Enter pip freeze and look for PyJWT, if found, the version listed should be < 2.0.0
If the listed version is not a supported version:
Consider upgrading to the latest version of AutoML SDK: pip install -U azureml-sdk[automl]
If that is not viable, uninstall PyJWT from the environment and install the right version as follows:
pip uninstall PyJWT in the command shell and enter y for confirmation.
Install using pip install 'PyJWT<2.0.0'.
Data access
For automated ML jobs, you need to ensure the file datastore that connects to your AzureFile storage has the appropriate authentication credentials. Otherwise, the following message results. Learn how to update your data access authentication credentials.
Error message:
Could not create a connection to the AzureFileService due to missing credentials. Either an Account Key or SAS token needs to be linked the default workspace blob store.
Data schema
When you try to create a new automated ML experiment via the Edit and submit button in the Azure Machine Learning studio, the data schema for the new experiment must match the schema of the data that was used in the original experiment. Otherwise, an error message similar to the following results. Learn more about how to edit and submit experiments from the studio UI.
Error message non-vision experiments:  Schema mismatch error: (an) additional column(s): "Column1: String, Column2: String, Column3: String", (a) missing column(s)
Error message for vision datasets: Schema mismatch error: (an) additional column(s): "dataType: String, dataSubtype: String, dateTime: Date, category: String, subcategory: String, status: String, address: String, latitude: Decimal, longitude: Decimal, source: String, extendedProperties: String", (a) missing column(s): "image_url: Stream, image_details: DataRow, label: List" Vision dataset error(s): Vision dataset should have a target column with name 'label'. Vision dataset should have labelingProjectType tag with value as 'Object Identification (Bounding Box)'.
Databricks
See How to configure an automated ML experiment with Databricks (Azure Machine Learning SDK v1).
Forecasting R2 score is always zero
This issue arises if the training data provided has time series that contains the same value for the last n_cv_splits + forecasting_horizon data points.
If this pattern is expected in your time series, you can switch your primary metric to normalized root mean squared error.
Failed deployment
For versions <= 1.18.0 of the SDK, the base image created for deployment may fail with the following error: ImportError: cannot import name cached_property from werkzeug.
The following steps can work around the issue:
Download the model package
Unzip the package
Deploy using the unzipped assets
Azure Functions application
Automated ML does not currently support Azure Functions applications.
Sample notebook failures
If a sample notebook fails with an error that property, method, or library does not exist:
Ensure that the correct kernel has been selected in the Jupyter Notebook. The kernel is displayed in the top right of the notebook page. The default is azure_automl. The kernel is saved as part of the notebook. If you switch to a new conda environment, you need to select the new kernel in the notebook.
For Azure Notebooks, it should be Python 3.6.
For local conda environments, it should be the conda environment name that you specified in automl_setup.
To ensure the notebook is for the SDK version that you are using,
Check the SDK version by executing azureml.core.VERSION in a Jupyter Notebook cell.
You can download previous version of the sample notebooks from GitHub with these steps:
Select the Branch button
Navigate to the Tags tab
Select the version
Experiment throttling
If you have over 100 automated ML experiments, this may cause new automated ML experiments to have long run times.
VNet Firewall Setting Download Failure
If you are under virtual networks (VNets), you may run into model download failures when using AutoML NLP. This is because network traffic is blocked from downloading the models and tokenizers from Azure CDN. To unblock this, please allow list the below URLs in the "Application rules" setting of the VNet firewall policy:
aka.ms
https://automlresources-prod.azureedge.net
Please follow the instructions here to configure the firewall settings.
Instructions for configuring workspace under vnet are available here.
Next steps
Learn more about how to train a regression model with Automated machine learning or how to train using Automated machine learning on a remote resource.
Learn more about how and where to deploy a model.