ibm-research/WatsonxDocsQARetrieval huggingface dataset:
# watsonx\.governance offering plan options #

The watsonx\.governance service enables responsible, transparent, and explainable AI\.

The available plans depend on the region where you are provisioning the service from the IBM Cloud catalog\.

<!-- <ul> -->

 *  In the Dallas region, provision a [watsonx\.governance plan](https://dataplatform.cloud.ibm.com/docs/content/wsj/model/wos-plan-options.html?context=cdpaas&locale=en#wos-plan-options-xgov-plans)\.
 *  In the Frankfurt region, provision an [Watson OpenScale](https://dataplatform.cloud.ibm.com/docs/content/wsj/model/wos-plan-options2.html) plan\.

<!-- </ul> -->

## watsonx\.governance plans (Dallas only) ##

Watsonx\.governance offers a free Lite plan and a paid Essentials plan\.

With watsonx\.governance you can:

<!-- <ul> -->

 *  Evaluate machine learning models for dimensions such as fairness, quality, or drift\.
 *  Define AI use cases in a collaborative, open way to define a business problem and track the solution\.
 *  Capture the details for machine learning models, in each stage of their lifecycle, and store the data in factsheets within an associated AI use case\.
 *  Maintain collections of AI uses cases in inventories, where you can manage access\.

<!-- </ul> -->

For Large Language Models in watsonx\.ai, you can also:

<!-- <ul> -->

 *  Evaluate prompt templates across multiple dimensions such as quality, Personally Identifable Information (PII) in prompt input and outputs, and Abuse or Profanity in Prompt input and ouput\.
 *  Monitor metrics for Large Language Model performance\.
 *  Automatically capture metadata in a Factsheet from development to deployment, for each stage in the lifecycle\.

<!-- </ul> -->

### watsonx\.governance Lite plan features ###

Lite plan features include:

<!-- <ul> -->

 *  Maximum of 200 resource units
 *  1 resource unit per predictive model evaluation
 *  1 resource unit per foundational model evaluation
 *  1 resource unit per global explanation, with a maximum of 500 local explanations
 *  1 resource unit per 500 local explanations
 *  Maximum of 1,000 records per evaluation
 *  Limit of 3 rows per use case
 *  Limit of 3 use cases
 *  Limit of 1 inventory

<!-- </ul> -->

### watsonx\.governance Essential plan features ###

Essential plan features include:

<!-- <ul> -->

 *  Maximum of 500 inventories
 *  1 resource unit per predictive model evaluation
 *  1 resource unit per foundational model evaluation
 *  1 resource unit per global explanation, with a maximum of 500 local explanations
 *  1 resource unit per 500 local explanations
 *  Maximum of 50,000 records per evaluation

<!-- </ul> -->

## Next steps ##

[Provisioning and launching the watsonx\.governance service](https://dataplatform.cloud.ibm.com/docs/content/wsj/model/wos-provision-launch.html)

**Parent topic:**[watsonx\.governance](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/aiopenscale.html)

<!-- </article "role="article" "> -->
# Watson OpenScale offering plan options #

The Watson OpenScale enables responsible, transparent, and explainable AI\.

With Watson OpenScale you can:

<!-- <ul> -->

 *  Evaluate machine learning models for dimensions such as fairness, quality, or drift\.
 *  Explore transactions to gain insights about your model\.

<!-- </ul> -->

## Watson OpenScale legacy offering plans ##

Important:The legacy offering plan for Watson OpenScale is available only in the Frankfurt region\. In the Dallas region, the [watsonx\.governance plans](https://dataplatform.cloud.ibm.com/docs/content/wsj/model/wos-plan-options.html) are available instead\.

### Watson OpenScale Standard v2 plan ###

Watson OpenScale offers a Standard v2 plan that charge users on a per model basis\.

There are no restrictions or limitations on payload data, feedback rows, or explanations under the Standard v2 instance\.

### Regional limitations ###

Watson OpenScale is not available in some regions\. See [Regional availability for services and features](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/regional-datactr.html) for more details\.

Note:The regional availability for every service can also be found in the [IBM watsonx catalog](https://dataplatform.cloud.ibm.com/data/catalog?target=services&context=cpdaas)\.

### Quota limits ###

To avoid performance issues and manage resources efficiently, Watson OpenScale sets the following quota limits:

<!-- <table> -->

| Asset              | Limit                    |
| ------------------ | ------------------------ |
| DataMart           | 100 per instance         |
| Service providers  | 100 per instance         |
| Integrated systems | 100 per instance         |
| Subscriptions      | 100 per service provider |
| Monitor instances  | 100 per subscription     |

<!-- </table ""> -->

Every asset in Watson OpenScale has a hard limitation of 10000 instances of the asset per service instance\.

### PostgreSQL databases for Watson OpenScale ###

You can use a PostgreSQL database for your Watson OpenScale instance\. PostgreSQL is a powerful, open source object\-relational database that is highly customizable and compliant with many security standards\.

If your model processes personally identifiable information (PII), use a PostgreSQL database for your model\. PostgreSQL is compliant with:

<!-- <ul> -->

 *  GDPR
 *  HIPAA
 *  PCI\-DSS
 *  SOC 1 Type 2
 *  SOC 2 Type 2
 *  ISO 27001
 *  ISO 27017
 *  ISO 27018
 *  ISO 27701

<!-- </ul> -->

## Next steps ##

[Managing the Watson OpenScale service](https://dataplatform.cloud.ibm.com/docs/content/wsj/model/wos-provision-launch.html)

**Parent topic:**[watsonx\.governance](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/aiopenscale.html)

<!-- </article "role="article" "> -->
# Sending model transactions in watsonx\.governance #

You must send model transactions from your deployment to watsonx\.governance to enable model evaluations\.

To generate accurate results for your model evaluations constantly, watsonx\.governance must continue to receive new data from your deployment\. watsonx\.governance provides different methods that you can use to send transactions for model evaluations\.

## Importing data ##

When you review evaluation results in watsonx\.governance, you can import data by selecting **Evaluate now** in the **Actions** menu to import payload and feedback data for your model evaluations\.

![Analyze prompt template evaluation results](https://dataplatform.cloud.ibm.com/docs/content/wsj/model/images/wos-eval-results-prod-spaces.png)

For pre\-production models, you must upload a CSV file that contains examples of input and output data\. To run evaluations with imported data, you must map prompt variables to the associated columns in your CSV file and select **Upload and evaluate** as shown in the following example:

![Upload CSV file](https://dataplatform.cloud.ibm.com/docs/content/wsj/model/images/wos-map-prompt-variables-preprod.png)

For production models, you can select **Upload payload data** or **Upload feedback data** in the **Import test data** window to upload a CSV file as shown in the following example:

![Import test data](https://dataplatform.cloud.ibm.com/docs/content/wsj/model/images/wos-import-test-data-prod-space.png)

The CSV file must contain labeled columns that match the columns in your payload and feedback schemas\. When your upload completes successfully, you can select **Evaluate now** to run your evaluations with your imported data\.

## Using endpoints ##

For production models, Watson OpenScale supports endpoints that you can use to provide data in formats that enable evaluations\. You can use the payload logging endpoint to send scoring requests for drift evaluations and use the feedback logging endpoint to provide feedback data for quality evaluations\. For more information about the data formats, see [Managing data for model evaluations](https://dataplatform.cloud.ibm.com/docs/content/wsj/model/wos-manage-data.html#it-dbo-active)\.

**Parent topic:**[Managing data for model evaluations in Watson OpenScale](https://dataplatform.cloud.ibm.com/docs/content/wsj/model/wos-manage-data.html)

<!-- </article "role="article" "> -->
# Validating your data in Data Refinery #

At any time after you've added data to Data Refinery, you can validate your data\. Typically, you'll want to do this at multiple points in the refinement process\.

To validate your data:

<!-- <ol> -->

1.  From Data Refinery, click the **Profile** tab\.
2.  Review the metrics for each column\.
3.  Take appropriate actions, as described in the following sections, depending on what you learn\.

<!-- </ol> -->

## Frequency ##

Frequency is the number of times that a value, or a value in a specified range, occurs\. Each frequency distribution (bar) shows the count of unique values in a column\.

Review the frequency distribution to find anomalies in your data\. If you want to cleanse your data of those anomalies, simply remove the values\.

For Integer and Date/Time columns, you can customize the number of bins (groupings) that you want to see\. In the default multi\-column view, the maximum is 20\. If you expand the frequency chart row, the maximum is 50\.

## Statistics ##

Statistics are a collection of quantitative data\. The statistics for each column show the minimum, maximum, mean, and number of unique values in that column\.

Depending on a column's data type, the statistics for each column will vary slightly\. For example, statistics for a column of data type integer have minimum, maximum, and mean values while statistics for a column of data type string have minimum length, maximum length, and mean length values\.

**Parent topic:**[Refining data](https://dataplatform.cloud.ibm.com/docs/content/wsj/refinery/refining_data.html)

<!-- </article "role="article" "> -->
# Troubleshooting Data Refinery #

Use this information to resolve questions about using Data Refinery\.

<!-- <ul> -->

 *  [Cannot refine data from an Excel data asset](https://dataplatform.cloud.ibm.com/docs/content/wsj/refinery/ts_index.html?context=cdpaas&locale=en#dr-excel)
 *  [Data Refinery flow job fails with a large data asset](https://dataplatform.cloud.ibm.com/docs/content/wsj/refinery/ts_index.html?context=cdpaas&locale=en#bigdata-dr)

<!-- </ul> -->

## Cannot refine data from an Excel data asset ##

The Data Refinery flow might fail if it cannot read the data\. Confirm the format of the Excel file\. By default, the first line of the file is treated as the header\. You can change this setting in the Flow settings ![settings icon](https://dataplatform.cloud.ibm.com/docs/content/wsj/refinery/images/settings.png)\. Go to the **Source data sets** tab and click the overflow menu (![overflow menu](https://dataplatform.cloud.ibm.com/docs/content/wsj/refinery/images/actions.png)) next to the data source, and select **Edit format**\. You can also specify the first line property, which designates which row is the first row in the data set to be read\. Changing these properties affects how the data is displayed in Data Refinery as well as the Data Refinery job run and flow output\.

## Data Refinery flow job fails with a large data asset ##

If your Data Refinery flow job fails with a large data asset, try these troubleshooting tips to fix the problem:

<!-- <ul> -->

 *  Instead of using a project data asset as the target of the Data Refinery flow (default), use Cloud storage\. For example, IBM Cloud Object Storage, Amazon S3, or Google Cloud Storage\.
 *  Select a **Spark & R** environment for the Data Refinery flow job or create a new **Spark & R** environment template\.

<!-- </ul> -->

<!-- </article "role="article" "> -->
# Accessing data in AWS through access points from a notebook #

In IBM watsonx you can access data stored in AWS S3 buckets through access points from a notebook\.

Run the notebook in an environment in IBM watsonx\. Create an internet\-enabled access point to connect to the S3 bucket\.

## Connecting to AWS S3 data through an internet\-enabled access point ##

You can access data in an AWS S3 bucket through an internet\-enabled access point in any AWS region\.

To access S3 data through an internet\-enabled access point:

<!-- <ol> -->

1.  Create an access point for your S3 bucket\. See [Creating access points](https://docs.aws.amazon.com/AmazonS3/latest/dev/creating-access-points.html)\.
    
    Set the network origin to `Internet`.
2.  After the access point is created, make a note of the Amazon resource name (ARN) for the access point\. Example: `ARN: arn:aws:s3:us-east-1:675068711478:accesspoint/cust-data-bucket-internet-ap`\. You will need to enter the ARN in your notebook\.

<!-- </ol> -->

## Accessing AWS S3 data from your notebook ##

The following sample code snippet shows you how to access AWS data from your notebook by using an access point:

    import boto3
    import pandas as pd
    
    # use an access key and a secret that has access to the bucket
    access_key="..."  
    secret="..." 
    
    s3_client = boto3.client('s3', aws_access_key_id=access_key, aws_secret_access_key=secret)
    
    #the Amazon resource name (ARN) of the access point
    arn = "..." 
    # the file you want to retrieve
    fileName="customers.csv"
    
    response = s3_client.get_object(Bucket=arn, Key=fileName)
    s3FileStream = response["Body"]
    #for other file types, change the line below to use the appropriate read_() method from pandas
    customerDF = pd.read_csv(s3FileStream)

**Parent topic:**[Loading and accessing data in a notebook](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/load-and-access-data.html)

<!-- </article "role="article" "> -->
# Key management by application #

This topic describes how to manage column encryption keys by application\. It explains how to provide master keys and how to write and read encrypted data using these master keys\.

## Providing master keys ##

To provide master keys:

<!-- <ol> -->

1.  Pass the explicit master keys, in the following format:
    
        parameter name: "encryption.key.list"
         parameter value: "<master key ID>:<master key (base64)> , <master key ID>:<master key (base64)>.."
    
    For example:
    
        sc.hadoopConfiguration.set("encryption.key.list" , "k1:iKwfmI5rDf7HwVBcqeNE6w== , k2:LjxH/aXxMduX6IQcwQgOlw== , k3:rnZHCxhUHr79Y6zvQnxSEQ==")
    
    The length of master keys before base64 encoding can be 16, 24 or 32 bytes (128, 192 or 256 bits).

<!-- </ol> -->

## Writing encrypted data ##

To write encrypted data:

<!-- <ol> -->

1.  Specify which columns to encrypt, and which master keys to use:
    
        parameter name:  "encryption.column.keys"
         parameter value: "<master key ID>:<column>,<column>;<master key ID>:<column> .."
2.  Specify the footer key:
    
        parameter name:  "encryption.footer.key"
         parameter value:  "<master key ID>"
    
    For example:
    
        dataFrame.write
         .option("encryption.footer.key" , "k1")
         .option("encryption.column.keys" , "k2:SSN,Address;k3:CreditCard")
         .parquet("<path to encrypted files>")
    
    Note:`"<path to encrypted files>"` must contain the string `.encrypted` in the URL, for example `/path/to/my_table.parquet.encrypted`. If either the `"encryption.column.keys"` parameter or the `"encryption.footer.key"` parameter is not set, an exception will be thrown.

<!-- </ol> -->

## Reading encrypted data ##

The required metadata is stored in the encrypted Parquet files\.

To read the encrypted data:

<!-- <ol> -->

1.  Provide the encryption keys:
    
        sc.hadoopConfiguration.set("encryption.key.list" , "k1:iKwfmI5rDf7HwVBcqeNE6w== , k2:LjxH/aXxMduX6IQcwQgOlw== , k3:rnZHCxhUHr79Y6zvQnxSEQ==")
2.  Call the regular parquet read commands, such as:
    
        val dataFrame = spark.read.parquet("<path to encrypted files>")
    
    Note:`"<path to encrypted files>"` must contain the string `.encrypted` in the URL, for example `/path/to/my_table.parquet.encrypted`.

<!-- </ol> -->

**Parent topic:**[Parquet encryption](https://dataplatform.cloud.ibm.com/docs/content/wsj/spark/parquet-encryption.html)

<!-- </article "role="article" "> -->
# Time series analysis #

A time series is a sequence of data values measured at successive, though not necessarily regular, points in time\. The time series library allows you to perform various key operations on time series data, including segmentation, forecasting, joins, transforms, and reducers\.

The library supports various time series types, including numeric, categorical, and arrays\. Examples of time series data include:

<!-- <ul> -->

 *  Stock share prices and trading volumes
 *  Clickstream data
 *  Electrocardiogram (ECG) data
 *  Temperature or seismographic data
 *  Network performance measurements
 *  Network logs
 *  Electricity usage as recorded by a smart meter and reported via an Internet of Things data feed

<!-- </ul> -->

An entry in a time series is called an observation\. Each observation comprises a time tick, a 64\-bit integer that indicates when the observation was made, and the data that was recorded for that observation\. The recorded data can be either numerical, for example, a temperature or a stock share price, or categorical, for example, a geographic area\. A time series can but must not necessarily be associated with a time reference system (TRS), which defines the granularity of each time tick and the start time\.

The time series library is Python only\.

## Next step ##

<!-- <ul> -->

 *  [Using the time series library](https://dataplatform.cloud.ibm.com/docs/content/wsj/spark/time-series-lib-using.html)

<!-- </ul> -->

## Learn more ##

<!-- <ul> -->

 *  [Time series key functionality](https://dataplatform.cloud.ibm.com/docs/content/wsj/spark/time-series-key-functionality.html)

<!-- </ul> -->

<!-- </article "role="article" "> -->
# Exporting synthetic data #

Using *Synthetic Data Generator*, you can export synthetic data to remote data sources using connections or write data to a project (**Delimited** or **SAV**)\.

Double\-click the node to open its properties\. Various options are available, described as follows\. After running the node, you can find the data at the export location you specified\.

## Exporting to a project ##

Under **Export to**, select **This project** and then select the project path\. For **File type**, select either **Delimited** or **SAV**\.

## Exporting to a connection ##

Under **Export to**, select **Save to a connection** to open the Asset Browser and then select the connection to export to\. For a list of supported data sources, see [Creating synthetic data from imported data](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/import_data_sd.html)\.

## Setting the field delimiter, quote character, and decimal symbol ##

Different countries use different symbols to separate the integer part from the fractional part of a number and to separate fields in data\. For example, you might use a comma instead of a period to separate the integer part from the fractional part of numbers\. And, rather than using commas to separate fields in your data, you might use colons or tabs\. With a Data Asset import or export node, you can specify these symbols and other options\. Double\-click the node to open its properties and specify data formats as desired\. ![Export data field delimiters](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/images/sd-export-data-field-delimiters.png)

<!-- </article "role="article" "> -->
# Creating synthetic data from a custom data schema #

Using the *Synthetic Data Generator* graphical editor flow tool, you can generate a structured synthetic data set based on meta data, automatically or with user\-specified statistical distributions\. You can define the data within each table column, their distributions, and any correlations\. You can then export and review your synthetic data\.

Before you can use *generate* to create synthetic data, you need [to create a task](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/synthetic_data_overview_sd.html#create-synthetic)\.

1\. The **Generate synthetic tabular data flow** window opens\. Select use case **Create from custom data schema**\. Click **Next**\. ![Generate synthetic tabular data flow window](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/images/sd-generate-flow.png)

2\. Select **Generate options**\. You can use the *Synthetic Data Generator* graphical editor flow tool to specify the number of rows and add columns\. You can define properties and specify fields, storage types, statistical distributions, and distribution parameters\. Click **Next**\. ![Generate options](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/images/sd-generate-options.png)

3\. Select **Export data** to select the export file name and type\. For more information, see [Exporting data](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/export_data_sd.html)\. Click **Next**\. ![Export data](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/images/sd-generate-export.png)

4\. Select **Review** to check your selection and make any updates before generating your synthetic data\. Click **Save and run**\. ![Review data](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/images/sd-generate-review.png)

## Learn more ##

[Creating synthetic data from production data](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/mask_mimic_data_sd.html)

<!-- </article "role="article" "> -->
# Creating synthetic data from production data #

Using the *Synthetic Data Generator* graphical editor flow tool, you can generate a structured synthetic data set based on your production data\. You can import data, *anonymize*, *mimic* (to generate synthetic data), export, and review your data\.

Before you can use *mimic* and *mask* to create synthetic data, you need [to create a task](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/synthetic_data_overview_sd.html#create-synthetic)\.

1\. The **Generate synthetic tabular data flow** window opens\. Select use case **Leverage your existing data**\. Click **Next**\. ![Generate synthetic tabular data flow window](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/images/sd-mimic-mask-flow.png)

2\. Select **Import data**\. You can also drag\-and\-drop a data file into your project\. You can also select data from a project\. For more information, see [Importing data](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/import_data_sd.html)\. ![Import data](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/images/sd-import-data.png)

3\. Once you have imported your data, you can use the *Synthetic Data Generator* graphical flow editor tool to *anonymize* your production data, masking the data\. You can disguise column names, column values, or both, when working with data that is to be included in a model downstream of the node\. For example, you can use bank customer data and hide marital status\. ![Anonymize data](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/images/sd-anonymize.png)

4\. You can then use the *Synthetic Data Generator* tool to *mimic* your production data\. This will generate synthetic data, based on your production data, using a set of candidate statistical distributions to modify each column in your data\. ![Mimic data](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/images/sd-mimic-options.png)

5\. You can export your synthetic data and review it\. For more information, see [Exporting synthetic data](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/export_data_sd.html)\. ![Export data](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/images/sd-export.png)

## Learn more ##

[Creating synthetic data from a custom data schema](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/generate_data_sd.html)

<!-- </article "role="article" "> -->
# Creating Synthetic data #

Use the graphical flow editor tool *Synthetic Data Generator* to generate synthetic tabular data based on production data or a custom data schema using visual flows and modeling algorithms\.

To create synthetic data, the first option is to use the *Synthetic Data Generator* graphical flow editor tool to *mask* and *mimic* production data, and then to load the result into a different location\.

The second option is to use the *Synthetic Data Generator* graphical flow editor to *generate* synthetic data from a custom data schema using visual flows and modeling algorithms\.

This image shows an overview of the *Synthetic Data Generator* graphical flow editor\. ![Synthetic Data Generator overview](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/images/sd-overview.png)

**Data format** Learn more about [Creating synthetic data from imported data](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/import_data_sd.html)\.

**Data size** : The *Synthetic Data Generator* environment can import up to ~2\.5GB of data\.

## Prerequisites ##

Before you can create synthetic data, you need [to create a project](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/projects.html)\.

## Create synthetic data ##

1\. Access the *Synthetic Data Generator* tool from within a project\. To select a new asset, open a tool, and create an asset, click **New asset**\.

2\. Select **All > Prepare Data > Generate synthetic tabular data** from the *What do you want to do?* window\. ![What do you want to do window](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/images/sd-what-do-you-want.png)

3\. The **Generate synthetic tabular data** window opens\. Add a name for the asset and a description (optional)\. Click **Create**\. The flow will open and it might take a minute to create a new session for the flow\. ![Generate synthetic tabular data flow asset](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/images/sd-generate-synthetic-tabular-data-flow-asset.png)

4\. The **Welcome to Synthetic Data Generator** wizard opens\. You can choose to get started as a first time or experienced user\.  
![Synthetic Data Generator Get started wizard](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/images/sd-wizard.png)

5\. If you choose to get started as a first time user, the **Generate synthetic tabular data flow** window opens\. ![Generate synthetic tabular data flow window](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/images/sd-mimic-mask-flow.png)

## Learn more ##

<!-- <ul> -->

 *  [Creating synthetic data from production data](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/mask_mimic_data_sd.html)
 *  [Creating synthetic data from a custom data schema](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/generate_data_sd.html)
 *  Try the [Generate synthetic tabular data tutorial](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/get-started-generate-data.html)

<!-- </ul> -->

<!-- </article "role="article" "> -->
# IBM Cloud services status #

If you're having a problem with one of your services, go to the IBM Cloud Status page\. The Status page shows unplanned incidents, planned maintenance, announcements, and security bulletin notifications about key events that affect the IBM Cloud platform, infrastructure, and major services\.

You can find the Status page by logging in to the IBM Cloud console\. Click **Support** from the menu bar, and then click **View cloud status** from the Support Center\. Or, you can access the page directly at [IBM Cloud \- Status](https://cloud.ibm.com/status?type=incident&component=ibm-cloud-platform&selected=status)\. Search for the service to view its status\.

## Learn more ##

[Viewing cloud status](https://cloud.ibm.com/docs/get-support?topic=get-support-viewing-cloud-status)

<!-- </article "role="article" "> -->
# Troubleshooting connections #

Use these solutions to resolve problems that you might encounter with connections\.

## IBM Db2 for z/OS: Error retrieving the schema list when you try to connect to a Db2 for z/OS server ##

When you test the connection to a Db2 for z/OS server and the connection cannot retrieve the schema list, you might receive the following error:

    CDICC7002E: The assets request failed: CDICO2064E: The metadata for the column TABLE_SCHEM could not
     be obtained: Sql error: [jcc] [10300] Invalid parameter: Unknown column name
     TABLE_SCHEM. ERRORCODE=-4460, SQLSTATE=null

**Workaround:** On the Db2 for z/OS server, set the **DESCSTAT** subsystem parameter to `No`\. For more information, see [DESCRIBE FOR STATIC field (DESCSTAT subsystem parameter)](https://www.ibm.com/docs/SSEPEK_13.0.0/inst/src/tpc/db2z_ipf_descstat.html)\.

**Parent topic:**[Supported connections](https://dataplatform.cloud.ibm.com/docs/content/wsj/manage-data/conn_types.html)

<!-- </article "role="article" "> -->
# Troubleshooting #

If you encounter an issue in IBM watsonx, use the following resources to resolve the problem\.

<!-- <ul> -->

 *  [View IBM Cloud service status](https://dataplatform.cloud.ibm.com/docs/content/wsj/troubleshoot/service-status.html)
 *  [Troubleshoot connections](https://dataplatform.cloud.ibm.com/docs/content/wsj/troubleshoot/troubleshoot-conn.html)
 *  [Troubleshoot Data Refinery](https://dataplatform.cloud.ibm.com/docs/content/wsj/refinery/ts_index.html)
 *  [Troubleshoot Synthetic Data Generator](https://dataplatform.cloud.ibm.com/docs/content/wsj/troubleshoot/ts_sd.html)
 *  [Troubleshoot IBM Cloud Object Storage](https://dataplatform.cloud.ibm.com/docs/content/wsj/troubleshoot/troubleshoot-cos.html)
 *  [Troubleshoot Watson Machine Learning](https://dataplatform.cloud.ibm.com/docs/content/wsj/troubleshoot/ml_troubleshooting.html)
 *  [Troubleshoot Watson OpenScale](https://dataplatform.cloud.ibm.com/docs/content/wsj/troubleshoot/wos-troubleshoot.html)
 *  [Troubleshoot Watson Studio on IBM Cloud](https://dataplatform.cloud.ibm.com/docs/content/wsj/troubleshoot/wscloud-troubleshoot.html)
 *  [Known issues](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/known-issues.html)
 *  [Get help](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/get-help.html)

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# Troubleshooting Watson Studio on IBM Cloud #

You can use the following techniques to work around problems you might encounter with Watson Studio on IBM Cloud\.

## Project limit exceeded ##

### Symptoms ###

When you create a project, the following error occurs:

    The number of projects created by the authenticated user exceeds the designated limit.

### Possible Causes ###

The number of projects an authenticated user can create per data center (region) is 100\. The limit applies only to projects that a user creates\. Projects for which the user is listed as a collaborator are not included in this limit\.

### Possible Resolutions ###

Although most customers do not reach this limit, possible resolutions include:

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 *  Delete projects\.
 *  Any authenticated user can request a project limit increase by contacting [IBM Cloud Support](https://www.ibm.com/cloud/support), provided that an adequate justification is specified\.

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## Blank screen when loading ##

### Symptoms ###

A blank screen appears when you open Watson Studio\.

### Possible Causes ###

A cached version is loading\.

### Possible Resolutions ###

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1.  Clear the browser cache and cookies and re\-open Watson Studio\.
2.  Try a different type of browser\. For example, switch from Firefox to Chrome\.
3.  If the blank screen still occurs, [open a support case](https://cloud.ibm.com/unifiedsupport/supportcenter), generate a \.har file, compress it, and upload the compressed har file to the support case\.

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# Deploying and managing assets #

Use Watson Machine Learning to deploy models and solutions so that you can put them into productive use, then monitor the deployed assets for fairness and explainability\. You can also automate the AI lifecycle to keep your deployed assets current\.

## Completing the AI lifecycle ##

After you prepare your data and build then train models or solutions, you complete the AI lifecycle by deploying and monitoring your assets\.

![Overview of model workflow](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/images/ml-engineer-overview-wx.svg)

Deployment is the final stage of the lifecycle of a model or script, where you run your models and code\. Watson Machine Learning provides the tools that you need to deploy an asset, such as a predictive model, Python function\. You can also deploy foundation model assets, such as prompt templates, to put them into production\.

Following deployment, you can use model management tools to evaluate your models\. IBM Watson OpenScale tracks and measures outcomes from your AI models, and helps ensure they remain fair, explainable, and compliant\. Watson OpenScale also detects and helps correct the drift in accuracy when an AI model is in production\.

Finally, you can use IBM Watson Pipelines to manage your ModelOps processes\. Create a pipeline that automates parts of the AI lifecycle, such as training and deploying a machine learning model\.

## Next steps ##

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 *  To learn more about how to manage assets in a deployment space, see [Manage assets in a deployment space](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-spaces_local.html)\.
 *  To learn more about how to deploy assets from a deployment space, see [Deploy assets from a deployment space](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-deploy-general.html)\.
 *  To learn more how to deploy by using [Python client](https://ibm.github.io/watson-machine-learning-sdk/) or , see [Sample notebook](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-samples-overview.html)\.

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