Each metadata resource is associated with a specific MetadataSchema
.
To simplify the metadata resource creation process Vertex ML Metadata publishes predefined
types called system schemas for common ML concepts. System schemas live
under the namespace system
. You can access system schemas as MetadataSchema resources in
the Vertex ML Metadata API. Schemas are always versioned. The format of system
schemas is a subset of the OpenAPI 3.0 specification
.
How to use system schemas
Vertex AI uses system schemas to create metadata resources for tracking
your ML workflows. You can then filter and group resources in metadata queries
by using the schema_title
field. For more information about how to use filter functions, see Analyze Vertex ML Metadata
.
You can also use system schemas through the Vertex ML Metadata API to create metadata resources directly. You can identify a system schema by its schema title and schema version. Fields in system schemas are always considered optional. You aren't restricted to the predefined fields of system schemas and can also log additional arbitrary metadata to any metadata resource. For more information about using system schemas to create metadata resources, see Track Vertex ML Metadata .
List your schemas
To view a list of all your existing registered schemas using the following command.
REST
Before using any of the request data, make the following replacements:
- LOCATION_ID : Your region.
- PROJECT_ID : Your project ID .
HTTP method and URL:
GET https:// LOCATION_ID -aiplatform.googleapis.com/v1/projects/ PROJECT_ID /locations/ LOCATION_ID /metadataStores/default/metadataSchemas?pageSize=100&filter=schema_title=%22system*%22+OR+schema_title=%22google*%22
To send your request, expand one of these options:
You should receive a JSON response similar to the following:
{ "metadataSchemas": [ { "name": "projects/ PROJECT_ID /locations/ LOCATION_ID /metadataStores/default/metadataSchemas/system-resolver-execution-v0-0-1", "schemaVersion": "0.0.1", "schema": "title: system.ResolverExecution\ntype: object\n", "schemaType": "EXECUTION_TYPE", "createTime": "2022-07-27T17:41:35.634Z" }, { "name": "projects/ PROJECT_ID /locations/ LOCATION_ID //metadataStores/default/metadataSchemas/system-html-v0-0-1", "schemaVersion": "0.0.1", "schema": "title: system.HTML\ntype: object\n", "schemaType": "ARTIFACT_TYPE", "createTime": "2022-07-27T17:41:35.602Z" } }
Strict schema matching
Vertex ML Metadata supports two flags that allow schema authors to enforce strict schema matching.
additionalProperties
The additionalProperties
value can be true or false. Consistent with JSON
Schema, additionalProperties
defaults to true. This flag is set at the top
level of the schema. If it's set to false, no optional properties are
allowed. For example in the below schema, only the fields payload_format
and container_format
are accepted in the metadata based on this schema.
title
:
system.Dataset
version
:
0.0.1
type
:
object
additionalProperties
:
false
properties
:
container_format
:
type
:
string
payload_format
:
type
:
string
The above schema accepts the following metadata:
f
ields
{
key
:
'co
nta
i
ner
_
f
orma
t
'
value
:
{
s
tr
i
n
g_value
:
'Tex
t
'
}
}
f
ields
{
key
:
'payload_
f
orma
t
'
value
:
{
s
tr
i
n
g_value
:
'CSV'
}
}
However following metadata will be rejected:
f
ields
{
key
:
'co
nta
i
ner
_
f
orma
t
'
value
:
{
s
tr
i
n
g_value
:
'Tex
t
'
}
}
f
ields
{
key
:
'payload_
f
orma
t
'
value
:
{
s
tr
i
n
g_value
:
'CSV'
}
}
f
ields
{
key
:
'op
t
io
nal
_
f
ield'
value
:
{
s
tr
i
n
g_value
:
'op
t
io
nal
_value'
}
}
required
The required
keyword takes an array of zero or more strings. Consistent
with JSON Schema, the properties defined by the properties keyword are not
required. You can provide a list of required properties using the required
keyword. For example the following schema always requires container_format
.
Works on nested properties as well. For example following makes the container_format
required.
title
:
system.Dataset
version
:
0.0.1
type
:
object
required
:
[
'container_format'
]
properties
:
container_format
:
type
:
string
payload_format
:
type
:
string
The above schema accepts the following metadata:
f
ields
{
key
:
'co
nta
i
ner
_
f
orma
t
'
value
:
{
s
tr
i
n
g_value
:
'Tex
t
'
}
}
However following metadata will be rejected:
f
ields
{
key
:
'payload_
f
orma
t
'
value
:
{
s
tr
i
n
g_value
:
'CSV'
}
}
The schema supports nested properties where properties has a field of type
object. In a nested schema, the nested properties node can have a required
keyword. For example:
title
:
system.Dataset
version
:
0.0.1
type
:
object
properties
:
container_format
:
type
:
string
payload
:
type
:
string
nested_property
:
type
:
object
required
:
[
'property_1'
]
properties
:
property_1
:
type
:
integer
property_2
:
type
:
integer
The above schema accepts the following metadata, since the nested_property
field itself is not required.
f
ields
{
key
:
'co
nta
i
ner
_
f
orma
t
'
value
:
{
s
tr
i
n
g_value
:
'Tex
t
'
}
}
Following metadata is also valid.
f
ields
{
key
:
'
neste
d_proper
t
y'
value
:
{
s
tru
c
t
_value
{
f
ields
{
key
:
'proper
t
y_
1
'
value
:
{
nu
mber_value
:
1
}
}
f
ields
{
key
:
'proper
t
y_
2
'
value
:
{
nu
mber_value
:
1
}
}
}
}
}
However following metadata will be rejected:
f
ields
{
key
:
'
neste
d_proper
t
y'
value
:
{
s
tru
c
t
_value
{
f
ields
{
key
:
'proper
t
y_
2
'
value
:
{
nu
mber_value
:
1
}
}
}
}
}
System schema examples
The following examples are common system schemas that are available for immediate use.
Artifact
system.Artifact
is a generic schema that can hold metadata about any artifact.
No specific fields are defined in this schema.
title
:
system.Artifact
version
:
0.0.1
type
:
object
Dataset
system.Dataset
represents a container of data that was either consumed or
produced by an ML workflow step. A dataset can point to either a file location
or a query, for example a BigQuery URI.
title
:
system.Dataset
version
:
0.0.1
type
:
object
properties
:
container_format
:
type
:
string
description
:
"Format
of
the
container.
Examples
include
'TFRecord',
'Text',
or
'Parquet'."
payload_format
:
type
:
string
description
:
"Format
of
the
payload.
For
example,
'proto:TFExample',
'CSV',
or
'JSON'."
Model
system.Model
represents a trained model. The URI of the model can point to a
file location (PPP, Cloud Storage bucket, local drive) or an API resource
such as the Model resource in Vertex AI API.
title
:
system.Model
version
:
0.0.1
type
:
object
properties
:
framework
:
type
:
string
description
:
"The
framework
type.
For
example:
'TensorFlow'
or
'Scikit-Learn'."
framework_version
:
type
:
string
description
:
"The
framework
version.
For
example:
'1.15'
or
'2.1'."
payload_format
:
type
:
string
description
:
"The
format
of
the
Model
payload,
for
example:
'SavedModel'
or
'TFLite'."
Metrics
system.Metrics
represents evaluation metrics produced during an ML workflow.
Metrics are application and use case dependent and can consist of simple scalar
metrics like accuracy or complex metrics that are stored elsewhere in the system.
title
:
system.Metrics
version
:
0.0.1
type
:
object
properties
:
type
:
accuracy
:
type
:
number
description
:
"Optional
summary
metric
describing
accuracy
of
a
model."
precision
:
type
:
number
description
:
"Optional
summary
metric
describing
precision
of
a
model."
recall
:
type
:
number
description
:
"Optional
summary
metric
describing
the
recall
of
a
model."
f1score
:
type
:
number
description
:
"Optional
summary
metric
describing
the
f1-score
of
a
model."
mean_absolute_error
:
type
:
number
description
:
"Optional
summary
metric
describing
the
mean
absolute
error
of
a
model."
mean_squared_error
:
type
:
number
description
:
"Optional
summary
metric
describing
the
mean-squared
error
of
a
model."
