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ApiKeyExpirationTime

ApiKeyExpirationTime()

Fields:

  • ONE_MONTH
  • THREE_MONTHS
  • SIX_MONTHS
  • ONE_YEAR
  • NEVER

BaseML3Enum

BaseML3Enum()

Base class for all enums in the ML3 Platform SDK

BooleanLicenceFeature

BooleanLicenceFeature()

Boolean licence feature

Fields: - EXPLAINABILITY Whether the company has access to explainability reports - MONITORING Whether the company has monitoring feature enabled - MONITORING_METRICS Whether the company has monitoring metrics feature enabled - SEGMENTED_MONITORING Whether the company has segmented monitoring feature enabled - RETRAINING Whether the company has retraining feature enabled - TOPIC_ANALYSIS Whether the company has topic analysis feature enabled - RAG_EVALUATION Whether the company has RAG evaluation feature enabled - LLM_SECURITY Whether the company has LLM security feature enabled - BUSINESS Whether the company has business feature enabled

ColumnRole

ColumnRole()

Column role enum Describe the role of a column

Fields: - INPUT - INPUT_MASK - METADATA - PREDICTION - TARGET - ERROR - ID - TIME_ID - INPUT_ADDITIONAL_EMBEDDING - TARGET_ADDITIONAL_EMBEDDING - PREDICTION_ADDITIONAL_EMBEDDING - USER_INPUT - RETRIEVED_CONTEXT

ColumnSubRole

ColumnSubRole()

This enum describes the subrole of a column in the data schema For instance, it's used in RAG tasks to distinguish between user input and retrieved context

Subroles for ColumnRole.INPUT in RAG settings: - RAG_USER_INPUT - RAG_RETRIEVED_CONTEXT - RAG_SYS_PROMPT

Subroles for ColumnRole.INPUT in TIMESERIES settings: - SEASONALITY - TREND - REGRESSOR

Subroles for ColumnRole.PREDICTION: - MODEL_PROBABILITY - OBJECT_LABEL_PREDICTION - OBJECT_TEXT_PREDICTION

Subroles for ColumnRole.TARGET: - OBJECT_LABEL_TARGET - OBJECT_TEXT_TARGET

Currency

Currency()

Currency of to use for the Task

Fields: - EURO - DOLLAR

DataBatchType

DataBatchType()

Defines the type of the uploaded data batch.

Fields:

TRAINING VALIDATION TEST PRODUCTION

DataStructure

DataStructure()

Represents the typology of the data to send

Fields:

  • TABULAR
  • IMAGE
  • TEXT
  • EMBEDDING

DataType

DataType()

Data type enum Describe data type of input

Fields: - FLOAT - STRING - CATEGORICAL - ARRAY_1 - ARRAY_2 - ARRAY_3

DetectionEventActionType

DetectionEventActionType()

Fields:

  • DISCORD_NOTIFICATION
  • SLACK_NOTIFICATION
  • EMAIL_NOTIFICATION
  • TEAMS_NOTIFICATION
  • MQTT_NOTIFICATION
  • RETRAIN
  • NEW_PLOT_CONFIGURATION
  • AWS_EVENT_BRIDGE_NOTIFICATION
  • GCP_PUBSUB_NOTIFICATION
  • AZURE_EVENT_GRID_NOTIFICATION

DetectionEventSeverity

DetectionEventSeverity()

Fields:

  • LOW
  • MEDIUM
  • HIGH

DetectionEventType

DetectionEventType()

Fields:

  • WARNING_OFF
  • WARNING_ON
  • DRIFT_ON
  • DRIFT_OFF

ExternalIntegration

ExternalIntegration()

An integration with a 3rd party service provider

Fields: - AWS - GCP - AZURE - AWS_COMPATIBLE - GOOGLE_GENAI - GOOGLE_VERTEXAI - OPENAI - AZURE_OPENAI - ANTHROPIC

FileType

FileType()

Fields:

  • CSV
  • JSON
  • PARQUET
  • PNG
  • JPG
  • NPY

FolderType

FolderType()

Type of folder

Fields

  • UNCOMPRESSED
  • TAR
  • ZIP

ImageMode

ImageMode()

Image mode enumeration

Fields: - RGB - RGBA - GRAYSCALE

JobStatus

JobStatus()

Enum containing all the job's status that a client can see

Fields:

  • IDLE
  • STARTING
  • RUNNING
  • COMPLETED
  • ERROR

KPIStatus

KPIStatus()

Fields:

  • NOT_INITIALIZED
  • OK
  • WARNING
  • DRIFT

ModelMetricName

ModelMetricName()

Name of the model metrics that is associated with the model

Fields: - RMSE - RSQUARE - ACCURACY - AVERAGE_PRECISION

MonitoringEvaluationMetric

MonitoringEvaluationMetric()

Metric computed at batch level and without referring to a task.

Differently from MonitoringMetric, that are extractions from quantities (MonitoringTarget) for each sample, the MonitoringEvaluationMetrics are computed for a set of data and not for the single sample.

Each task type has a list of metrics.

BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION

  • Accuracy
  • Precision
  • Recall
  • F1-score
  • ROC-AUC (requires model probability)
  • PR-AUC (requires model probability)
  • LogLoss (requires model probability)
  • Balanced Accuracy

REGRESSION

  • MAE
  • RMSE
  • R square

CLUSTERING

  • Silhouette score
  • Calinski-Harabasz Index
  • Adjusted Rand Index (requires ground truth)
  • NMI / V-measure (requires ground truth)

ANOMALY DETECTION

  • ROC-AUC (requires model probability)
  • PR-AUC (requires model probability)
  • FPR @ TPR (requires model probability)

TIMESERIES

  • MAE
  • RMSE
  • MAPE

MonitoringMetric

MonitoringMetric()

Tabular: - FEATURE

Text: - TEXT_TOXICITY - TEXT_EMOTION - TEXT_SENTIMENT - TEXT_LENGTH

Model probabilistic output: - MODEL_PERPLEXITY - MODEL_ENTROPY - MODEL_IMAGE_ENTROPY

Error: - LOG_LIKELIHOOD: likelihood of target sample for distribution induced by the model

Image: - IMAGE_BRIGHTNESS - IMAGE_CONTRAST - IMAGE_FOCUS - IMAGE_BLUR - IMAGE_COLOR_VARIATION - IMAGE_COLOR_CONTRAST

Object detection and semantic segmentation: (position wrt Cartesian axis with origin in the center of the image)

MonitoringStatus

MonitoringStatus()

Fields:

  • OK
  • WARNING
  • DRIFT

MonitoringTarget

MonitoringTarget()

Fields:

  • ERROR
  • INPUT
  • CONCEPT
  • PREDICTION
  • INPUT_PREDICTION
  • USER_INPUT
  • RETRIEVED_CONTEXT
  • USER_INPUT_RETRIEVED_CONTEXT
  • USER_INPUT_MODEL_OUTPUT
  • MODEL_OUTPUT_RETRIEVED_CONTEXT
  • CHARACTER_ERROR_RATE
  • WORD_ERROR_RATE

NumericLicenceFeature

NumericLicenceFeature()

Numeric licence feature

Fields: - MAX_TASKS Maximum number of tasks that the company can have - MAX_USERS Maximum number of users that the company can have - DAILY_DATA_BATCH_UPLOAD Maximum number of data batches that the company can upload in a day. Only considers production data batches.

OcrMode

OcrMode()

Ocr mode enumeration

Fields: - PLAIN_TEXT - WITH_LABELS

ProductKeyStatus

ProductKeyStatus()

Status of a product key

Fields:: - NEW = generated but not yet used product key - VALIDATING = validation requested from client - IN_USE = validated product key, client activated

RetrainTriggerType

RetrainTriggerType()

Enumeration of the possible retrain triggers

Fields:: - AWS_EVENT_BRIDGE - GCP_PUBSUB - AZURE_EVENT_GRID

SegmentOperator

SegmentOperator()

Segment operator for segmentation rules. Fields: - IN: the given rule is verified if the field is in the list of values - OUT: the given rule is verified if the field is not in the list of values

SemanticSegTargetType

SemanticSegTargetType()

Format of the target and prediction for the semantic segmentation task.

POLYGON: each identified object is represented by the vertices of the polygon

StoragePolicy

StoragePolicy()

Enumeration that specifies the storage policy for the data sent to ML cube Platform

Fields: cloud it needs to read data

StoringDataType

StoringDataType()

Fields:

  • HISTORICAL
  • REFERENCE
  • PRODUCTION
  • KPI

SubscriptionType

SubscriptionType()

Type of subscription plan of a company

Fields:: - CLOUD: subscription plan for web app or sdk access - EDGE: subscription plan for edge deployment

SuggestionType

SuggestionType()

Enum to specify the preferred type of suggestion

Fields: - SAMPLE_WEIGHTS - RESAMPLED_DATASET

TaskType

TaskType()

Fields:

  • REGRESSION
  • CLASSIFICATION_BINARY
  • CLASSIFICATION_MULTICLASS
  • CLASSIFICATION_MULTILABEL
  • RAG
  • OBJECT_DETECTION
  • SEMANTIC_SEGMENTATION
  • CLUSTERING

TextLanguage

TextLanguage()

Enumeration of text language used in nlp tasks.

Fields: - ITALIAN - ENGLISH - MULTILANGUAGE

TimeseriesMode

TimeseriesMode()

Define how regressors, seasonality or trend are used in the timeseries.

UserCompanyRole

UserCompanyRole()

Fields:

  • COMPANY_OWNER
  • COMPANY_ADMIN
  • COMPANY_USER
  • COMPANY_NONE

UserProjectRole

UserProjectRole()

Fields:

  • PROJECT_ADMIN
  • PROJECT_USER
  • PROJECT_VIEW