diff --git a/data-science/components/evaluate.yml b/data-science/components/evaluate.yml
deleted file mode 100644
index f02ba4c..0000000
--- a/data-science/components/evaluate.yml
+++ /dev/null
@@ -1,24 +0,0 @@
-# <component>
-$schema: https://azuremlschemas.azureedge.net/latest/commandComponent.schema.json
-name: evaluate_model
-display_name: evaluate-model
-type: command
-inputs:
-    model_name:
-        type: string
-    model_input:
-        type: uri_folder
-    test_data: 
-        type: uri_folder
-outputs:
-    evaluation_output: 
-        type: uri_folder
-code: ../src/evaluate
-environment: azureml:taxi-train-env@latest
-command: >-
-    python evaluate.py 
-    --model_name ${{inputs.model_name}} 
-    --model_input ${{inputs.model_input}} 
-    --test_data ${{inputs.test_data}} 
-    --evaluation_output ${{outputs.evaluation_output}}
-# </component>
\ No newline at end of file
diff --git a/data-science/components/prep.yml b/data-science/components/prep.yml
deleted file mode 100644
index e4bdc29..0000000
--- a/data-science/components/prep.yml
+++ /dev/null
@@ -1,30 +0,0 @@
-# <component>
-$schema: https://azuremlschemas.azureedge.net/latest/commandComponent.schema.json
-name: prep_data
-display_name: prep-data
-type: command
-inputs:
-    raw_data: 
-        type: uri_file
-    enable_monitoring:
-        type: string
-    table_name:
-        type: string
-outputs:
-    train_data: 
-        type: uri_folder
-    val_data: 
-        type: uri_folder
-    test_data:
-        type: uri_folder 
-code: ../src/prep
-environment: azureml:taxi-train-env@latest
-command: >-
-    python prep.py 
-    --raw_data ${{inputs.raw_data}} 
-    --train_data ${{outputs.train_data}}
-    --val_data ${{outputs.val_data}}
-    --test_data ${{outputs.test_data}}
-    --enable_monitoring ${{inputs.enable_monitoring}}
-    --table_name ${{inputs.table_name}}
-# </component>
\ No newline at end of file
diff --git a/data-science/components/register.yml b/data-science/components/register.yml
deleted file mode 100644
index a1aead2..0000000
--- a/data-science/components/register.yml
+++ /dev/null
@@ -1,24 +0,0 @@
-# <component>
-$schema: https://azuremlschemas.azureedge.net/latest/commandComponent.schema.json
-name: register_model
-display_name: register-model
-type: command
-inputs:
-    model_name: 
-        type: string
-    model_path: 
-        type: uri_folder
-    evaluation_output: 
-        type: uri_folder
-outputs:
-    model_info_output_path: 
-        type: uri_folder
-code: ../src/register
-environment: azureml:taxi-train-env@latest
-command: >-
-    python register.py 
-    --model_name ${{inputs.model_name}} 
-    --model_path ${{inputs.model_path}} 
-    --evaluation_output ${{inputs.evaluation_output}}
-    --model_info_output_path ${{outputs.model_info_output_path}}
-# </component>
\ No newline at end of file
diff --git a/data-science/components/train.yml b/data-science/components/train.yml
deleted file mode 100644
index 8bbc273..0000000
--- a/data-science/components/train.yml
+++ /dev/null
@@ -1,18 +0,0 @@
-# <component>
-$schema: https://azuremlschemas.azureedge.net/latest/commandComponent.schema.json
-name: train_model
-display_name: train-model
-type: command
-inputs:
-    train_data: 
-        type: uri_folder
-outputs:
-    model_output: 
-        type: uri_folder
-code: ../src/train
-environment: azureml:taxi-train-env@latest
-command: >-
-    python train.py 
-    --train_data ${{inputs.train_data}} 
-    --model_output ${{outputs.model_output}}
-# </component>
\ No newline at end of file
diff --git a/mlops/azureml/azureml-sdkv2.ipynb b/mlops/azureml/azureml-sdkv2.ipynb
index e723c01..c3a9797 100644
--- a/mlops/azureml/azureml-sdkv2.ipynb
+++ b/mlops/azureml/azureml-sdkv2.ipynb
@@ -88,7 +88,7 @@
       "metadata": {
         "collapsed": false,
         "gather": {
-          "logged": 1670200031039
+          "logged": 1671554468232
         },
         "jupyter": {
           "outputs_hidden": false,
@@ -166,7 +166,7 @@
         "from azure.ai.ml.entities import AmlCompute\n",
         "\n",
         "my_cluster = AmlCompute(\n",
-        "    name=\"cpu-cluster-CA\",\n",
+        "    name=\"cpu-cluster\",\n",
         "    type=\"amlcompute\", \n",
         "    size=\"STANDARD_DS3_V2\", \n",
         "    min_instances=0, \n",
@@ -181,7 +181,7 @@
       "metadata": {
         "collapsed": false,
         "gather": {
-          "logged": 1670200031797
+          "logged": 1671554474840
         },
         "jupyter": {
           "outputs_hidden": false,
@@ -264,7 +264,7 @@
         "\n",
         "my_data = Data(\n",
         "    path=\"../../data/taxi-data.csv\",\n",
-        "    type=AssetTypes.URI_FILE,\n",
+        "    type=\"uri_file\",\n",
         "    description=\"Taxi dataset\",\n",
         "    name=\"taxi-data\"\n",
         ")\n",
@@ -275,7 +275,7 @@
       "metadata": {
         "collapsed": false,
         "gather": {
-          "logged": 1670200033270
+          "logged": 1671554479153
         },
         "jupyter": {
           "outputs_hidden": false,
@@ -333,7 +333,7 @@
       "metadata": {
         "collapsed": false,
         "gather": {
-          "logged": 1670200035753
+          "logged": 1671554483854
         },
         "jupyter": {
           "outputs_hidden": false,
@@ -397,36 +397,106 @@
       "cell_type": "code",
       "source": [
         "from azure.ai.ml.dsl import pipeline\n",
-        "from azure.ai.ml import Input, Output, load_component\n",
+        "from azure.ai.ml import Input, Output, command\n",
         "from azure.ai.ml.constants import AssetTypes, InputOutputModes\n",
         "\n",
-        "# Create pipeline job\n",
-        "parent_dir = \"../../data-science/components\"\n",
         "\n",
-        "# 1. Load components\n",
-        "prepare_data = load_component(source=parent_dir + \"/prep.yml\")\n",
-        "train_model = load_component(source=parent_dir + \"/train.yml\")\n",
-        "evaluate_model = load_component(source=parent_dir + \"/evaluate.yml\")\n",
-        "register_model = load_component(source=parent_dir + \"/register.yml\")\n",
+        "# 1. Define components\n",
+        "\n",
+        "prep_data = command( \n",
+        "    name=\"prep_data\",\n",
+        "    display_name=\"prep-data\",\n",
+        "    code=\"../../data-science/src/prep\",\n",
+        "    command=\"python prep.py \\\n",
+        "            --raw_data ${{inputs.raw_data}} \\\n",
+        "            --train_data ${{outputs.train_data}}  \\\n",
+        "            --val_data ${{outputs.val_data}} \\\n",
+        "            --test_data ${{outputs.test_data}} \\\n",
+        "            --enable_monitoring ${{inputs.enable_monitoring}} \\\n",
+        "            --table_name ${{inputs.table_name}}\",\n",
+        "    environment=\"taxi-train-env@latest\",\n",
+        "    inputs={\n",
+        "        \"raw_data\": Input(type=\"uri_file\"),\n",
+        "        \"enable_monitoring\": Input(type=\"string\"),\n",
+        "        \"table_name\": Input(type=\"string\")\n",
+        "        },\n",
+        "    outputs={\n",
+        "        \"train_data\": Output(type=\"uri_folder\"),\n",
+        "        \"val_data\": Output(type=\"uri_folder\"),\n",
+        "        \"test_data\": Output(type=\"uri_folder\"),\n",
+        "        }\n",
+        ")\n",
+        "\n",
+        "train_model = command( \n",
+        "    name=\"train_model\",\n",
+        "    display_name=\"train-model\",\n",
+        "    code=\"../../data-science/src/train\",\n",
+        "    command=\"python train.py \\\n",
+        "            --train_data ${{inputs.train_data}} \\\n",
+        "            --model_output ${{outputs.model_output}}\",\n",
+        "    environment=\"taxi-train-env@latest\",\n",
+        "    inputs={\"train_data\": Input(type=\"uri_folder\")},\n",
+        "    outputs={\"model_output\": Output(type=\"uri_folder\")}\n",
+        ")\n",
+        "\n",
+        "evaluate_model = command(\n",
+        "    name=\"evaluate_model\",\n",
+        "    display_name=\"evaluate-model\",\n",
+        "    code=\"../../data-science/src/evaluate\",\n",
+        "    command=\"python evaluate.py \\\n",
+        "            --model_name ${{inputs.model_name}} \\\n",
+        "            --model_input ${{inputs.model_input}} \\\n",
+        "            --test_data ${{inputs.test_data}} \\\n",
+        "            --evaluation_output ${{outputs.evaluation_output}}\",\n",
+        "    environment=\"taxi-train-env@latest\",\n",
+        "    inputs={\n",
+        "        \"model_name\": Input(type=\"string\"),\n",
+        "        \"model_input\": Input(type=\"uri_folder\"),\n",
+        "        \"test_data\": Input(type=\"uri_folder\")\n",
+        "        },\n",
+        "    outputs={\n",
+        "        \"evaluation_output\": Output(type=\"uri_folder\")\n",
+        "        }\n",
+        ")\n",
+        "\n",
+        "register_model = command(\n",
+        "    name=\"register_model\",\n",
+        "    display_name=\"register-model\",\n",
+        "    code=\"../../data-science/src/register\",\n",
+        "    command=\"python register.py \\\n",
+        "            --model_name ${{inputs.model_name}} \\\n",
+        "            --model_path ${{inputs.model_path}} \\\n",
+        "            --evaluation_output ${{inputs.evaluation_output}} \\\n",
+        "            --model_info_output_path ${{outputs.model_info_output_path}}\",\n",
+        "    environment=\"taxi-train-env@latest\",\n",
+        "    inputs={\n",
+        "        \"model_name\": Input(type=\"string\"),\n",
+        "        \"model_path\": Input(type=\"uri_folder\"),\n",
+        "        \"evaluation_output\": Input(type=\"uri_folder\")\n",
+        "        },\n",
+        "    outputs={\n",
+        "        \"model_info_output_path\": Output(type=\"uri_folder\")\n",
+        "        }\n",
+        ")\n",
         "\n",
         "# 2. Construct pipeline\n",
         "@pipeline()\n",
         "def taxi_training_pipeline(raw_data, enable_monitoring, table_name):\n",
         "    \n",
-        "    prepare = prepare_data(\n",
+        "    prep = prep_data(\n",
         "        raw_data=raw_data,\n",
         "        enable_monitoring=enable_monitoring, \n",
         "        table_name=table_name\n",
         "    )\n",
         "\n",
         "    train = train_model(\n",
-        "        train_data=prepare.outputs.train_data\n",
+        "        train_data=prep.outputs.train_data\n",
         "    )\n",
         "\n",
         "    evaluate = evaluate_model(\n",
         "        model_name=\"taxi-model\",\n",
         "        model_input=train.outputs.model_output,\n",
-        "        test_data=prepare.outputs.test_data\n",
+        "        test_data=prep.outputs.test_data\n",
         "    )\n",
         "\n",
         "\n",
@@ -437,15 +507,15 @@
         "    )\n",
         "\n",
         "    return {\n",
-        "        \"pipeline_job_train_data\": prepare.outputs.train_data,\n",
-        "        \"pipeline_job_test_data\": prepare.outputs.test_data,\n",
+        "        \"pipeline_job_train_data\": prep.outputs.train_data,\n",
+        "        \"pipeline_job_test_data\": prep.outputs.test_data,\n",
         "        \"pipeline_job_trained_model\": train.outputs.model_output,\n",
         "        \"pipeline_job_score_report\": evaluate.outputs.evaluation_output,\n",
         "    }\n",
         "\n",
         "\n",
         "pipeline_job = taxi_training_pipeline(\n",
-        "    Input(type=AssetTypes.URI_FILE, path=\"taxi-data@latest\"), \"false\", \"taximonitoring\"\n",
+        "    Input(type=\"uri_file\", path=\"taxi-data@latest\"), \"false\", \"taximonitoring\"\n",
         ")\n",
         "\n",
         "# set pipeline level compute\n",
@@ -458,7 +528,7 @@
       "metadata": {
         "collapsed": false,
         "gather": {
-          "logged": 1670200036044
+          "logged": 1671554553702
         },
         "jupyter": {
           "outputs_hidden": false,
@@ -475,7 +545,7 @@
       "cell_type": "code",
       "source": [
         "pipeline_job = ml_client.jobs.create_or_update(\n",
-        "    pipeline_job, experiment_name=\"pipeline_samples\"\n",
+        "    pipeline_job, experiment_name=\"taxi-training\"\n",
         ")\n",
         "pipeline_job"
       ],
@@ -484,7 +554,7 @@
       "metadata": {
         "collapsed": false,
         "gather": {
-          "logged": 1670200062228
+          "logged": 1671554563966
         },
         "jupyter": {
           "outputs_hidden": false,
@@ -564,7 +634,7 @@
         "\n",
         "# create an online endpoint\n",
         "online_endpoint = ManagedOnlineEndpoint(\n",
-        "    name=\"taxi-online-endpoint-3\", \n",
+        "    name=\"taxi-online-ep\", \n",
         "    description=\"Taxi online endpoint\",\n",
         "    auth_mode=\"aml_token\",\n",
         ")\n",
@@ -577,7 +647,7 @@
       "metadata": {
         "collapsed": false,
         "gather": {
-          "logged": 1669584576485
+          "logged": 1671555421278
         },
         "jupyter": {
           "outputs_hidden": false,
@@ -624,7 +694,7 @@
         "\n",
         "blue_deployment = ManagedOnlineDeployment(\n",
         "    name=\"blue\",\n",
-        "    endpoint_name=\"taxi-online-endpoint-3\",\n",
+        "    endpoint_name=\"taxi-online-ep\",\n",
         "    model=model,\n",
         "    instance_type=\"Standard_DS2_v2\",\n",
         "    instance_count=1,\n",
@@ -639,7 +709,7 @@
       "metadata": {
         "collapsed": false,
         "gather": {
-          "logged": 1669584886619
+          "logged": 1671557131286
         },
         "jupyter": {
           "outputs_hidden": false
@@ -922,7 +992,7 @@
     },
     "language_info": {
       "name": "python",
-      "version": "3.10.4",
+      "version": "3.10.6",
       "mimetype": "text/x-python",
       "codemirror_mode": {
         "name": "ipython",