Add Chat-Completion/Text-Generation MaaS FineTuning Samples.

.github/CODEOWNERS

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 /cli/monitoring/generation-safety-quality-monitoring.yaml @ahughes-msft @yunjie-hub
 /cli/monitoring/model-monitoring-with-collected-data.yaml @ahughes-msft @yunjie-hub
 /cli/monitoring/out-of-box-monitoring.yaml @ahughes-msft @yunjie-hub
+/cli/jobs/finetuning @amltres @sasum @marici
 /sdk/python/foundation-models/benchmarking/evaluating_claude_models.ipynb @arun-rajora
 /sdk/python/foundation-models/benchmarking/evaluating_oai_models.ipynb @arun-rajora
 /sdk/python/foundation-models/benchmarking/evaluation_pipelines/* @arun-rajora
@@ -23,6 +24,7 @@ sdk/python/foundation-models/cohere/command_tools-langchain.ipynb @stewart-co @k
 /sdk/python/foundation-models/nixtla/ @AzulGarza
 /sdk/python/foundation-models/healthcare-ai/ @jmerkow @ivantarapov
 /sdk/python/assets/data/versioning.ipynb @ShakutaiGit
+/sdk/python/jobs/finetuning @amltres @sasum @marici
 
 #### files referenced in docs (DO NOT EDIT, except for Docs team!!!) #############################################################################################
 /cli/assets/component/train.yml @sdgilley @msakande @Blackmist @ssalgadodev @lgayhardt @fbsolo-ms1  

cli/jobs/finetuning/standalone/finetuning-job-cliv2-readme.md

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+# Running a Fine-Tuning Job from CLI
+
+This guide provides instructions on how to run a fine-tuning job using the Azure Machine Learning CLI v2.
+
+## Prerequisites
+
+1. **Azure CLI**: Ensure you have the Azure CLI installed. If not, you can install it from [here](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli).
+
+2. **Azure Machine Learning CLI v2**: Install the Azure Machine Learning CLI extension v2.
+   ```bash
+   az extension add -n ml -y
+   ```
+
+3. **Azure Subscription**: Ensure you have an active Azure subscription and the necessary permissions to create and manage resources.
+
+4. **Resource Group and Workspace**: Ensure you have an Azure resource group and an Azure Machine Learning workspace. If not, you can create them using the following commands:
+
+      ```bash 
+      az group create --name <resource-group-name> --location <location>
+      az ml workspace create --name <workspace-name> --resource-group <resource-group-name> --location <location>
+      ```
+
+**Note**: MaaS finetuning is supported in following regions due to capacity constraints.
+* Llama models can only be finetuned in westus3 region.
+* All other models which support MaaS Finetuning can be finetuned in eastus2 region
+
+### Running the Fine-Tuning Job
+To run the fine-tuning job, use the following command:
+
+```bash
+az ml job create --file text-generation-finetuning-amlcompute.yaml --resource-group <<resource-group-name>> --workspace-name <<azureml-workspace-or-project-name>> --name "ft-maap-llama3-instance-types-1209-01"
+```
+
+#### Command Breakdown
+* az ml job create: Command to create and run a job in Azure Machine Learning.
+* --file text-generation-finetuning-amlcompute.yaml: Specifies the YAML file that defines the job configuration.
+* --resource-group <<resource-group-name>>: Specifies the Azure resource group.
+* --workspace-name <<azureml-workspace-or-project-name>>: Specifies the Azure Machine Learning workspace.
+* --name "ft-maap-llama3-instance-types-1209-01": Specifies the name of the job.
+
+##### InputData
+Each sample has input data files provided.
+* train.jsonl - This contains training data.
+* validation.jsonl - This contains validation data.
+
+Note that these files are for demo purposes only.
+
+Sample Yaml file for generating FineTuningJob using azureml CLIV2
+
+**Text Generation FineTuning (Model-As-A-Service)**
+1. [finetuning-with-amlcompute](./model-as-a-service/text-generation/text-generation-finetuning.yaml)
+
+**ChatCompletion FineTuning (Model-As-A-Service)**
+1. [finetuning-with-amlcompute](./model-as-a-service/chat-completion/chat-completion-finetuning.yaml)
+
+

cli/jobs/finetuning/standalone/model-as-a-service/chat-completion/chat-completion-finetuning.yaml

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+type: finetuning
+
+name: "Phi-3-mini-4k-instruct-with-amlcompute"
+experiment_name: "Phi-3-mini-4k-instruct-finetuning-experiment"
+display_name: "Phi-3-mini-4k-instruct-display-name"
+task: chat_completion
+model_provider: custom
+model: 
+  path: "azureml://registries/azureml/models/Phi-3-mini-4k-instruct/versions/14"
+  type: mlflow_model
+training_data: train.jsonl
+validation_data:
+  path: validation.jsonl
+  type: uri_file
+hyperparameters:
+  num_train_epochs: "1"
+  per_device_train_batch_size: "1"
+  learning_rate: "0.00002"
+properties:
+  my_property: "my_value"
+tags:
+  foo_tag: "bar"
+outputs:
+  registered_model:
+    name: "Phi-3-mini-4k-instruct-finetuned-model"
+    type: mlflow_model

cli/jobs/finetuning/standalone/model-as-a-service/chat-completion/readme.md

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+## How to run FineTuningJob using AzureML CLIV2?
+[Running FineTuningJob Using AzureML CLIV2](../../finetuning-job-cliv2-readme.md)

cli/jobs/finetuning/standalone/model-as-a-service/text-generation/readme.md

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+## How to run FineTuningJob using AzureML CLIV2?
+[Running FineTuningJob Using AzureML CLIV2](../../finetuning-job-cliv2-readme.md)

cli/jobs/finetuning/standalone/model-as-a-service/text-generation/text-generation-finetuning.yaml

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+type: finetuning
+
+name: "llama-3-8B-with-compute"
+experiment_name: "llama-3-8B-finetuning-experiment"
+display_name: "llama3-8B-display-name"
+task: text_completion
+model_provider: custom
+model: 
+  path: "azureml://registries/azureml-meta/models/Meta-Llama-3-8B/versions/8"
+  type: mlflow_model
+training_data: train.jsonl
+validation_data:
+  path: validation.jsonl
+  type: uri_file
+hyperparameters:
+  num_train_epochs: "1"
+  per_device_train_batch_size: "1"
+  learning_rate: "0.00002"
+properties:
+  my_property: "my_value"
+tags:
+  foo_tag: "bar"
+outputs:
+  registered_model:
+    name: "llama-3-8B-finetuned-model"
+    type: mlflow_model