Merge pull request #212924 from sdgilley/sdg-migration

prmerger-automator[bot] · web-flow · commit b90dd63bed5c · 2022-09-28T22:40:17.000Z
change terminology first set
diff --git a/articles/machine-learning/migrate-to-v2-assets-data.md b/articles/machine-learning/migrate-to-v2-assets-data.md
@@ -1,7 +1,7 @@
 ---
-title: 'Migrate data management from SDK v1 to v2'
+title: 'Upgrade data management to SDK v2'
 titleSuffix: Azure Machine Learning
-description: Migrate data management from v1 to v2 of Azure Machine Learning SDK
+description: Upgrade data management from v1 to v2 of Azure Machine Learning SDK
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: mldata
@@ -13,7 +13,7 @@ ms.reviewer: sgilley
 ms.custom: migration
 ---
 
-# Migrate data management from SDK v1 to v2
+# Upgrade data management to SDK v2
 
 In V1, an AzureML dataset can either be a `Filedataset` or a `Tabulardataset`.
 In V2, an AzureML data asset can be a `uri_folder`, `uri_file` or `mltable`.
diff --git a/articles/machine-learning/migrate-to-v2-assets-model.md b/articles/machine-learning/migrate-to-v2-assets-model.md
@@ -1,7 +1,7 @@
 ---
-title:  Migrate model management from SDK v1 to SDK v2
+title:  Upgrade model management to SDK v2
 titleSuffix: Azure Machine Learning
-description: Migrate model management from v1 to v2 of Azure Machine Learning SDK
+description: Upgrade model management from v1 to v2 of Azure Machine Learning SDK
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -13,7 +13,7 @@ ms.reviewer: sgilley
 ms.custom: migration
 ---
 
-# Migrate model management from SDK v1 to SDK v2
+# Upgrade model management to SDK v2
 
 This article gives a comparison of scenario(s) in SDK v1 and SDK v2.
 
diff --git a/articles/machine-learning/migrate-to-v2-command-job.md b/articles/machine-learning/migrate-to-v2-command-job.md
@@ -1,7 +1,7 @@
 ---
-title: 'Migrate script run from SDK v1 to SDK v2'
+title: 'Upgrade script run to SDK v2'
 titleSuffix: Azure Machine Learning
-description: Migrate how to run a script from SDK v1 to SDK v2
+description: Upgrade how to run a script from SDK v1 to SDK v2
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -13,13 +13,13 @@ ms.reviewer: sgilley
 ms.custom: migration
 ---
 
-# Migrate script run from SDK v1 to SDK v2
+# Upgrade script run to SDK v2
 
 In SDK v2, "experiments" and "runs" are consolidated into jobs.
 
 A job has a type. Most jobs are command jobs that run a `command`, like `python main.py`. What runs in a job is agnostic to any programming language, so you can run `bash` scripts, invoke `python` interpreters, run a bunch of `curl` commands, or anything else.
 
-To migrate, you'll need to change your code for submitting jobs to SDK v2. What you run _within_ the job doesn't need to be migrated to SDK v2. However, it's recommended to remove any code specific to Azure ML from your model training scripts. This separation allows for an easier transition between local and cloud and is considered best practice for mature MLOps. In practice, this means removing `azureml.*` lines of code. Model logging and tracking code should be replaced with MLflow. For more details, see [how to use MLflow in v2](how-to-use-mlflow-cli-runs.md).
+To upgrade, you'll need to change your code for submitting jobs to SDK v2. What you run _within_ the job doesn't need to be migrated to SDK v2. However, it's recommended to remove any code specific to Azure ML from your model training scripts. This separation allows for an easier transition between local and cloud and is considered best practice for mature MLOps. In practice, this means removing `azureml.*` lines of code. Model logging and tracking code should be replaced with MLflow. For more details, see [how to use MLflow in v2](how-to-use-mlflow-cli-runs.md).
 
 This article gives a comparison of scenario(s) in SDK v1 and SDK v2.
 
diff --git a/articles/machine-learning/migrate-to-v2-deploy-endpoints.md b/articles/machine-learning/migrate-to-v2-deploy-endpoints.md
@@ -1,7 +1,7 @@
 ---
-title: Migrate endpoints from SDK v1 to SDK v2
+title: Upgrade deployment endpoints to SDK v2
 titleSuffix: Azure Machine Learning
-description: Migrate deployment endpoints from v1 to v2 of Azure Machine Learning SDK
+description: Upgrade deployment endpoints from v1 to v2 of Azure Machine Learning SDK
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -13,7 +13,7 @@ ms.reviewer: sgilley
 ms.custom: migration
 ---
 
-# Migrate deployment endpoints from SDK v1 to SDK v2
+# Upgrade deployment endpoints to SDK v2
 
 We newly introduced [online endpoints](concept-endpoints.md) and batch endpoints as v2 concepts. There are several deployment funnels such as managed online endpoints, [kubernetes online endpoints](how-to-attach-kubernetes-anywhere.md) (including AKS and Arch-enabled Kubernetes) in v2, and ACI and AKS webservices in v1. In this article, we'll focus on the comparison of deploying to ACI webservices (v1) and managed online endpoints (v2).
 
diff --git a/articles/machine-learning/migrate-to-v2-execution-automl.md b/articles/machine-learning/migrate-to-v2-execution-automl.md
@@ -1,5 +1,5 @@
 ---
-title: Migrate AutoML from SDK v1 to SDK v2
+title: Upgrade AutoML to SDK v2
 titleSuffix: Azure Machine Learning
 description: Migrate AutoML from v1 to v2 of Azure Machine Learning SDK
 services: machine-learning
@@ -13,7 +13,7 @@ ms.reviewer: sgilley
 ms.custom: migration
 ---
 
-# Migrate AutoML from SDK v1 to SDK v2
+# Upgrade AutoML to SDK v2
 
 In SDK v2, "experiments" and "runs" are consolidated into jobs.
 
diff --git a/articles/machine-learning/migrate-to-v2-execution-hyperdrive.md b/articles/machine-learning/migrate-to-v2-execution-hyperdrive.md
@@ -1,7 +1,7 @@
 ---
-title: 'Migrate from v1 to v2: '
+title: Upgrade hyperparameter tuning to SDK v2
 titleSuffix: Azure Machine Learning
-description: Migrate from v1 to v2 of Azure Machine Learning SDK
+description: Upgrade hyperparameter tuning from v1 to v2 of Azure Machine Learning SDK
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -13,7 +13,7 @@ ms.reviewer: sgilley
 ms.custom: migration
 ---
 
-# Migrate hyperparameter tuning from SDK v1 to SDK v2
+# Upgrade hyperparameter tuning to SDK v2
 
 In SDK v2, tuning hyperparameters are consolidated into jobs.
 
diff --git a/articles/machine-learning/migrate-to-v2-execution-parallel-run-step.md b/articles/machine-learning/migrate-to-v2-execution-parallel-run-step.md
@@ -1,7 +1,7 @@
 ---
-title: Migrate parallel run step from SDK v1 to SDK v2
+title: Upgrade parallel run step to SDK v2
 titleSuffix: Azure Machine Learning
-description: Migrate parallel run step from v1 to v2 of Azure Machine Learning SDK
+description: Upgrade parallel run step from v1 to v2 of Azure Machine Learning SDK
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -13,15 +13,15 @@ ms.reviewer: sgilley
 ms.custom: migration
 ---
 
-# Migrate parallel run step from SDK v1 to SDK v2
+# Upgrade parallel run step to SDK v2
 
 In SDK v2, "Parallel run step" is consolidated into job concept as `parallel job`. Parallel job keeps the same target to empower users to accelerate their job execution by distributing repeated tasks on powerful multi-nodes compute clusters. On top of parallel run step, v2 parallel job provides extra benefits:
 
 - Flexible interface, which allows user to define multiple custom inputs and outputs for your parallel job. You can connect them with other steps to consume or manage their content in your entry script 
 - Simplify input schema, which replaces `Dataset` as input by using v2 `data asset` concept. You can easily use your local files or blob directory URI as the inputs to parallel job.
 - More powerful features are under developed in v2 parallel job only. For example, resume the failed/canceled parallel job to continue process the failed or unprocessed mini-batches by reusing the successful result to save duplicate effort.
 
-To migrate your current sdk v1 parallel run step to v2, you'll need to 
+To upgrade your current sdk v1 parallel run step to v2, you'll need to 
 
 - Use `parallel_run_function` to create parallel job by replacing `ParallelRunConfig` and `ParallelRunStep` in v1.
 - Migrate your v1 pipeline to v2. Then invoke your v2 parallel job as a step in your v2 pipeline. See [how to migrate pipeline from v1 to v2](migrate-to-v2-execution-pipeline.md) for the details about pipeline migration.
