• AOAI

• Basic

• Batch or Sequence Scoring Selector

• Batch Score

• Batch Scoring

• Batch Scoring Postprocess

• Batch Scoring Preprocess

• Benchmarking

• Demand Forecasting

• Distillation

• Embedding

• Feature Store

• Finetune

• Foundational Models

• Foundational Models Finetune

• High

• Low

• Medium

• Model Evaluation

• Model Monitoring

• Models

• NLP Multiclass

• NLP Multilabel

• Premium

• Responsible AI

• Retrieval Augmented Generation

• Sample

• Singularity

• Standard

• Validation File Checker

---

• action_analyzer_correlation_test

  Perform correlation test on different groups to generate actions.

• action_analyzer_identify_problem_traffic

  Separate bad queries into different groups.

• action_analyzer_metrics_calculation

  Calculate futher metrics for generating actions.

• action_analyzer_output_actions

  Merge and output actions.

• aoai_finetune_pipeline

  Pipeline component for proxy fine-tuning with AOAI

• aoai_finetuning

  Upload data to Azure OpenAI resource, finetune model and delete data

• automl_forecasting_inference

  Inference component for AutoML Forecasting.

• automl_hts_automl_training_step

• automl_hts_data_aggregation_step

• automl_hts_inference

  Enables inference for hts components.

• automl_hts_inference_collect_step

• automl_hts_inference_setup_step

• automl_hts_prs_inference_step

• automl_hts_training

  Enables AutoML Training for hts components.

• automl_hts_training_collect_step

• automl_hts_training_setup_step

• automl_many_models_inference

  Inference components for AutoML many model.

• automl_many_models_inference_collect_step

• automl_many_models_inference_setup_step

• automl_many_models_inference_step

• automl_many_models_training

  Enables AutoML many models training.

• automl_many_models_training_collection_step

• automl_many_models_training_setup_step

• automl_many_models_training_step

• automl_tabular_data_partitioning

  Enables dataset partitioning for AutoML many models and hierarchical timeseries solution accelerators using spark.

• batch_benchmark_config_generator

  Generates the config for the batch score component.

• batch_score_oss

• benchmark_embedding_model

  Component for benchmarking an embedding model via MTEB.

• chat_completion_datapreprocess

  Component to preprocess data for chat completion task. See docs to learn more.

• chat_completion_finetune

  Component to finetune Hugging Face pretrained models for chat completion task. The component supports optimizations such as LoRA, Deepspeed and ONNXRuntime for performance enhancement. See docs to learn more.

• chat_completion_model_import

  Component to import PyTorch / MLFlow model. See docs to learn more.

• chat_completion_pipeline

  Pipeline Component to finetune Hugging Face pretrained models for chat completion task. The component supports optimizations such as LoRA, Deepspeed and ONNXRuntime for performance enhancement. See docs to learn more.

• convert_model_to_mlflow

  Component converts models from supported frameworks to MLflow model packaging format

• data_delete

  Delete data file from Azure OpenAI resource

• data_drift_compute_metrics

  Compute data drift metrics given a baseline and a deployment's model data input.

• data_drift_signal_monitor

  Computes the data drift between a baseline and production data assets.

• data_quality_compute_metrics

  Compute data quality metrics leveraged by the data quality monitor.

• data_quality_data_statistics

  Compute data statistics leveraged by the data quality monitor.

• data_quality_metrics_joiner

  Join baseline and target data quality metrics into a single output.

• data_quality_signal_monitor

  Computes the data quality of a target dataset with reference to a baseline.

• data_upload

  Component to upload user's data from AzureML workspace to Azure OpenAI resource

• feature_attribution_drift_compute_metrics

  Feature attribution drift using model monitoring.

• feature_attribution_drift_signal_monitor

  Computes the feature attribution between a baseline and production data assets.

• feature_importance_metrics

  Feature importance for model monitoring.

• feature_retrieval

  Retrieval component to be used to retrieve offline features from feature store.

• finetune_submit

  Component to submit FT job to Azure OpenAI resource

• ft_nlp_common_validation

  Component to validate the finetune job against Validation Service

• ft_nlp_model_converter

  Component to convert the finetune job output to pytorch and mlflow model

• genai_mdc_preprocessor

  Filters the raw span log based on the window provided, and aggregates it to trace level.

• genai_token_statistics_compute_metrics

  Compute token statistics metrics.

• genai_token_statistics_signal_monitor

  Computes the token and cost metrics over LLM outputs.

• generation_safety_quality_signal_monitor

  Computes the content generation safety metrics over LLM outputs.

• gsq_annotation_compute_histogram

  Compute annotation histogram given a deployment's model data input.

• gsq_annotation_compute_metrics

  Compute annotation metrics given a deployment's model data input.

• gsq_input_schema_adaptor

  Adapt data to fit into GSQ component.

• import_model

  Import a model into a workspace or a registry

• llm_ingest_dataset_to_acs_basic

  Single job pipeline to chunk data from AzureML data asset, and create ACS embeddings index

• llm_ingest_dataset_to_acs_user_id

  Single job pipeline to chunk data from AzureML data asset, and create ACS embeddings index

• llm_ingest_dataset_to_faiss_basic

  Single job pipeline to chunk data from AzureML data asset, and create FAISS embeddings index

• llm_ingest_dataset_to_faiss_user_id

  Single job pipeline to chunk data from AzureML data asset, and create FAISS embeddings index

• llm_rag_crack_and_chunk

  Creates chunks no larger than chunk_size from input_data, extracted document titles are prepended to each chunk

LLM models have token limits for the prompts passed to them, this is a limiting factor at embedding time and even more limiting at prompt completion time as only so much context ca...

• llm_rag_crack_and_chunk_and_embed

  Creates chunks no larger than chunk_size from input_data, extracted document titles are prepended to each chunk

LLM models have token limits for the prompts passed to them, this is a limiting factor at embedding time and even more limiting at prompt completion time as only so much context ca...

• llm_rag_crack_chunk_embed_index_and_register

  Creates chunks no larger than chunk_size from input_data, extracted document titles are prepended to each chunk\n\n

LLM models have token limits for the prompts passed to them, this is a limiting factor at embedding time and even more limiting at prompt completion time as only so much contex...

• llm_rag_crawl_url

  Crawls the given URL and nested links to max_crawl_depth. Data is stored to output_path.

• llm_rag_create_faiss_index

  Creates a FAISS index from embeddings. The index will be saved to the output folder. The index will be registered as a Data Asset named asset_name if register_output is set to True.

• llm_rag_create_promptflow

  This component is used to create a RAG flow based on your mlindex data and best prompts. The flow will look into your indexed data and give answers based on your own data context. The flow also provides the capability to bulk test with any built-in or custom evaluation flows.

• llm_rag_data_import_acs

  Collects documents from Azure Cognitive Search Index, extracts their contents, saves them to a uri folder, and creates an MLIndex yaml file to represent the search index.

Documents collected can then be used in other components without having to query the ACS index again, allowing for a consiste...

• llm_rag_generate_embeddings

  Generates embeddings vectors for data chunks read from chunks_source.

chunks_source is expected to contain csv files containing two columns:

• "Chunk" - Chunk of text to be embedded
• "Metadata" - JSON object containing metadata for the chunk

If embeddings_container is supplied, input c...

• llm_rag_generate_embeddings_parallel

  Generates embeddings vectors for data chunks read from chunks_source.

chunks_source is expected to contain csv files containing two columns:

• "Chunk" - Chunk of text to be embedded
• "Metadata" - JSON object containing metadata for the chunk

If previous_embeddings is supplied, input ch...

• llm_rag_git_clone

  Clones a git repository to output_data path

• llm_rag_image_embed_index

  Embeds input images and stores it in Azure Cognitive Search index with metadata using Florence embedding resource. MLIndex is stored to output_path.

• llm_rag_qa_data_generation

  Generates a test dataset of questions and answers based on the input documents.

A chunk of text is read from each input document and sent to the specified LLM with a prompt to create a question and answer based on that text. These question, answer, and context sets are saved as either a csv or j...

• llm_rag_register_mlindex_asset

  Registers a MLIndex yaml and supporting files as an AzureML data asset

• llm_rag_register_qa_data_asset

  Registers a QA data csv or json and supporting files as an AzureML data asset

• llm_rag_update_acs_index

  Uploads embeddings into Azure Cognitive Search instance specified in acs_config. The Index will be created if it doesn't exist.

The Index will have the following fields populated:

• "id", String, key=True

• "content", String

• "contentVector", Collection(Single)

• "category", String

• "url",...

• llm_rag_update_cosmos_mongo_vcore_index

  Uploads embeddings into Azure Cosmos Mongo vCore collection/index specified in azure_cosmos_mongo_vcore_config. The collection/index will be created if it doesn't exist.

The collection/index will have the following fields populated:

• "_id", String, key=True

• "content", String

• "contentVec...

• llm_rag_update_milvus_index

  Uploads embeddings into Milvus collection/index specified in milvus_config. The collection/index will be created if it doesn't exist.

The collection/index will have the following fields populated:

• "id", String, key=True

• "content", String

• "contentVector", Collection(Single)

• "url", Str...

• llm_rag_update_pinecone_index

  Uploads embeddings into Pinecone index specified in pinecone_config. The Index will be created if it doesn't exist.

Each record in the Index will have the following metadata populated:

• "id", String

• "content", String

• "url", String

• "filepath", String

• "title", String

• "metadata_json_...

• llm_rag_validate_deployments

  Validates that completion model, embedding model, and Azure Cognitive Search resource deployments is successful and connections works. For default AOAI, it attempts to create the deployments if not valid or present. This validation is done only if customer is using Azure Open AI models or creatin...

• medical_image_embedding_datapreprocessing

  To generate embeddings for medical images.

• medimage_embedding_adapter_merge

  Integrate labels and generates classification model

• medimage_insight_ft_pipeline

  Pipeline Component to finetune MedImageInsight Model.

• medimgage_adapter_finetune

  Component to finetune the model using the medical image data

• medimgage_embedding_finetune

  Component to finetune the model using the medical image data

• mmdetection_image_objectdetection_instancesegmentation_pipeline

  Pipeline component for image object detection and instance segmentation using MMDetection models.

• model_data_collector_preprocessor

  Filters the data based on the window provided.

• model_monitor_action_analyzer

  Generate and output actions to the default datastore.

• model_monitor_action_detector

  Generate and output actions

• model_monitor_azmon_metric_publisher

  Azure Monitor Publisher for the computed model monitor metrics.

• model_monitor_compute_histogram

  Compute a histogram given an input data and associated histogram buckets.

• model_monitor_compute_histogram_buckets

  Compute histogram buckets given up to two datasets.

• model_monitor_create_manifest

  Creates the model monitor metric manifest.

• model_monitor_data_joiner

  Joins two data assets on the given columns for model monitor.

• model_monitor_evaluate_metrics_threshold

  Evaluate signal metrics against the threshold provided in the monitoring signal.

• model_monitor_feature_selector

  Selects features to compute signal metrics on.

• model_monitor_metric_outputter

  Output the computed model monitor metrics.

• model_monitor_output_metrics

  Output the computed model monitor metrics to the default datastore.

• model_performance_compute_metrics

  Compute model performance metrics leveraged by the model performance monitor.

• model_performance_signal_monitor

  Computes the model performance

• model_prediction

  Generate predictions on a given mlflow model for supported tasks.

• model_prediction_with_container

  Optimized Distributed inference component for LLMs.

• my_sample

• nlp_multiclass_datapreprocessing

  Component to preprocess data for automl nlp multiclass classification task

• nlp_multilabel_datapreprocessing

  Component to preprocess data for automl nlp multilabel classification task

• nlp_textclassification_multiclass

  Pipeline component for AutoML NLP Multiclass Text classification

• nlp_textclassification_multilabel

  Pipeline component for AutoML NLP Multilabel Text classification

• oss_chat_completion_finetune

  FTaaS component to finetune model for Chat Completion task

• oss_chat_completion_pipeline

  FTaaS Pipeline component for chat completion

• oss_distillation_batchscoring_datagen_pipeline

  Component to generate data from teacher model endpoint by invoking it in batch.

• oss_distillation_data_generation_batch_scoring_selector

  Component to select the Batch Scoring Selector based on the task type

• oss_distillation_data_generation_file_selector

  Component to select the Batch Scoring Selector based on the task type

• oss_distillation_data_generation_validation_file_checker

  Component to Check if the validation file is present or not

• oss_distillation_generate_data

  Component to generate data from teacher model enpoint

• oss_distillation_generate_data_batch_postprocess

  Component to prepare data returned from teacher model enpoint in batch

• oss_distillation_generate_data_batch_preprocess

  Component to prepare data to invoke teacher model enpoint in batch

• oss_distillation_seq_scoring_pipeline

  Component to generate data from teacher model enpoint(sequentially) and finetune student model on generated dataset

• oss_distillation_validate_pipeline

  Component to validate inputs to the distillation pipeline

• oss_text_generation_data_import

  FTaaS component to copy user training data to output

• oss_text_generation_finetune

  FTaaS component to finetune model for Text Generation task

• oss_text_generation_pipeline

  FTaaS Pipeline component for text generation

• prediction_drift_signal_monitor

  Computes the prediction drift between a baseline and a target data assets.

• question_answering_datapreprocess

  Component to preprocess data for question answering task. See docs to learn more.

• question_answering_finetune

  Component to finetune Hugging Face pretrained models for extractive question answering task. The component supports optimizations such as LoRA, Deepspeed and ONNXRuntime for performance enhancement. See docs to learn more.

• question_answering_model_import

  Component to import PyTorch / MLFlow model. See docs to learn more.

• question_answering_pipeline

  Pipeline Component to finetune Hugging Face pretrained models for extractive question answering task. The component supports optimizations such as LoRA, Deepspeed and ONNXRuntime for performance enhancement. See docs to learn more.

• rai_tabular_causal

  Add Causal to RAI Insights Dashboard Learn More

• rai_tabular_counterfactual

  Add Counterfactuals to RAI Insights Dashboard Learn More

• rai_tabular_erroranalysis

  Add Error Analysis to RAI Insights Dashboard Learn More

• rai_tabular_explanation

  Add Explanation to RAI Insights Dashboard Learn More

• rai_tabular_insight_constructor

  RAI Insights Dashboard Constructor Learn More

• rai_tabular_insight_gather

  Gather RAI Insights Dashboard Learn More

• rai_tabular_score_card

  Generate rai insight score card Learn More

• rai_text_insights

• rai_vision_insights

• summarization_datapreprocess

  Component to preprocess data for summarization task. See docs to learn more.

• summarization_finetune

  Component to finetune Hugging Face pretrained models for summarization task. The component supports optimizations such as LoRA, Deepspeed and ONNXRuntime for performance enhancement. See docs to learn more.

• summarization_model_import

  Component to import PyTorch / MLFlow model. See docs to learn more.

• summarization_pipeline

  Pipeline Component to finetune Hugging Face pretrained models for summarization task. The component supports optimizations such as LoRA, Deepspeed and ONNXRuntime for performance enhancement. See docs to learn more.

• text_classification_datapreprocess

  Component to preprocess data for single label classification task. See docs to learn more.

• text_classification_finetune

  Component to finetune Hugging Face pretrained models for text classification task. The component supports optimizations such as LoRA, Deepspeed and ONNXRuntime for performance enhancement. See docs to learn more.

• text_classification_model_import

  Component to import PyTorch / MLFlow model. See docs to learn more.

• text_classification_pipeline

  Pipeline component to finetune Hugging Face pretrained models for text classification task. The component supports optimizations such as LoRA, Deepspeed and ONNXRuntime for performance enhancement. See docs to learn more.

• text_generation_datapreprocess

  Component to preprocess data for text generation task

• text_generation_finetune

  Component to finetune model for Text Generation task

• text_generation_model_import

  Import PyTorch / MLFlow model

• text_generation_pipeline

  Pipeline component for text generation

• text_generation_pipeline_singularity_basic_high

  Pipeline component for text generation

• text_generation_pipeline_singularity_basic_low

  Pipeline component for text generation

• text_generation_pipeline_singularity_basic_medium

  Pipeline component for text generation

• text_generation_pipeline_singularity_premium_high

  Pipeline component for text generation

• text_generation_pipeline_singularity_premium_low

  Pipeline component for text generation

• text_generation_pipeline_singularity_premium_medium

  Pipeline component for text generation

• text_generation_pipeline_singularity_standard_high

  Pipeline component for text generation

• text_generation_pipeline_singularity_standard_low

  Pipeline component for text generation

• text_generation_pipeline_singularity_standard_medium

  Pipeline component for text generation

• token_classification_datapreprocess

  Component to preprocess data for token classification task. See docs to learn more.

• token_classification_finetune

  Component to finetune Hugging Face pretrained models for token classification task. The component supports optimizations such as LoRA, Deepspeed and ONNXRuntime for performance enhancement. See docs to learn more.

• token_classification_model_import

  Component to import PyTorch / MLFlow model. See docs to learn more.

• token_classification_pipeline

  Pipeline component to finetune Hugging Face pretrained models for token classification task. The component supports optimizations such as LoRA, Deepspeed and ONNXRuntime for performance enhancement. See docs to learn more.

• token_statistics_compute_metrics

  Compute token statistics metrics.

• transformers_image_classification_pipeline

  Pipeline component for image classification using HuggingFace transformers models.

• translation_datapreprocess

  Component to preprocess data for translation task. See docs to learn more.

• translation_finetune

  Component to finetune Hugging Face pretrained models for translation task. The component supports optimizations such as LoRA, Deepspeed and ONNXRuntime for performance enhancement. See docs to learn more.

• translation_model_import

  Component to import PyTorch / MLFlow model. See docs to learn more.

• translation_pipeline

  Pipeline component to finetune Hugging Face pretrained models for translation task. The component supports optimizations such as LoRA, Deepspeed and ONNXRuntime for performance enhancement. See docs to learn more.

• validation_trigger_import

  Component for enabling validation of import pipeline.