diff --git a/.github/workflows/pre_commits.yml b/.github/workflows/pre_commits.yml
index 8a18a15..2049626 100644
--- a/.github/workflows/pre_commits.yml
+++ b/.github/workflows/pre_commits.yml
@@ -13,7 +13,7 @@
 jobs:
   linting:
     name: Run pre-commit hooks on py3.6
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-20.04
     steps:
       #----------------------------------------------
       # Checkout, SetUp Python, Load Cache and Run PreCommitChecks
diff --git a/.github/workflows/unittest.yml b/.github/workflows/unittest.yml
deleted file mode 100644
index d2f05a2..0000000
--- a/.github/workflows/unittest.yml
+++ /dev/null
@@ -1,38 +0,0 @@
----
-name: Running unittest
-
-on:
-  # Trigger the workflow on push or pull request, but only for the master branch
-  push:
-    branches:
-      - master
-  pull_request:
-    branches:
-      - master
-  # Trigger the workflow on cron schedule
-  schedule:
-    - cron: '7 0 * * *'
-
-jobs:
-  unittest:
-    runs-on: ubuntu-latest
-    timeout-minutes: 10
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v1
-      - name: Setup Minikube
-        uses: manusa/actions-setup-minikube@v2.3.0
-        with:
-          minikube version: v1.16.0
-          kubernetes version: v1.19.2
-          github token: ${{ secrets.GITHUB_TOKEN }}
-      - name: Set up Python
-        uses: actions/setup-python@v1
-        with:
-          python-version: '3.6'
-      - name: Install python requirements
-        run: |
-          python -m pip install --upgrade pip
-          pip install -r requirements.txt
-      - name: Run unittest
-        run: ./run_tests.sh
diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
new file mode 100644
index 0000000..742e11f
--- /dev/null
+++ b/.github/workflows/unittests.yml
@@ -0,0 +1,49 @@
+---
+name: Running unittest
+
+on:
+  # Trigger the workflow on push or pull request, but only for the master branch
+  push:
+    branches:
+      - master
+  pull_request:
+    branches:
+      - master
+  # Trigger the workflow on cron schedule
+  schedule:
+    - cron: '7 0 * * *'
+
+jobs:
+  unittest:
+    runs-on: ubuntu-20.04
+    timeout-minutes: 20
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v1
+      - name: Setup Minikube
+        uses: manusa/actions-setup-minikube@v2.7.1
+        with:
+          minikube version: 'v1.26.1'
+          kubernetes version: 'v1.25.0'
+          github token: ${{ secrets.GITHUB_TOKEN }}
+      - name: Set up Python
+        uses: actions/setup-python@v1
+        with:
+          python-version: '3.6'
+      - name: Install python requirements
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+      - name: Run unittest - runners
+        run: ./run_tests.sh
+  approve:
+    needs: unittest
+    runs-on: ubuntu-20.04
+
+    steps:
+      - run: | # approve the pull request
+          curl --request POST \
+          --url https://api.github.com/repos/${{github.repository}}/pulls/${{github.event.number}}/reviews \
+          --header 'authorization: Bearer ${{ secrets.GITHUB_TOKEN }}' \
+          --header 'content-type: application/json' \
+          -d '{"event":"APPROVE"}'
diff --git a/docs/liminal/kubernetes/secret_util.md b/docs/liminal/kubernetes/secret_util.md
index 2bb44f3..cf46531 100644
--- a/docs/liminal/kubernetes/secret_util.md
+++ b/docs/liminal/kubernetes/secret_util.md
@@ -27,7 +27,6 @@
 name: k8s_secret_example
 secrets:
   - secret: aws
-    remote_path: "/secrets"
     local_path_file: "~/.aws/credentials"
 executors:
   - executor: k8s
@@ -40,14 +39,10 @@
     image: amazon/aws-cli:2.7.23
     executors: 2
     env_vars:
-      AWS_CONFIG_FILE: "/secrets/credentials"
-      AWS_PROFILE: "default"
-    secrets:
+      AWS_CONFIG_FILE: "/secret/credentials"
+    secret_mount:
       - secret: aws
-    mounts:
-      - mount: myaws-creds
-        volume: aws
-        path: /mnt/
+        remote_path: "/secret"
 pipelines:
   - pipeline: k8s_secret_example
     owner: Bosco Albert Baracus
diff --git a/examples/sagemaker_example/README.md b/examples/sagemaker_example/README.md
new file mode 100644
index 0000000..72e7a78
--- /dev/null
+++ b/examples/sagemaker_example/README.md
@@ -0,0 +1,51 @@
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements.  See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership.  The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License.  You may obtain a copy of the License at
+
+  http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied.  See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+# sageMaker
+
+Enable access to AWS STS AssumeRole:
+
+Go yo IAM->Role of your user and add under Trust Relationships
+```
+{
+    "Version": "2012-10-17",
+    "Statement": [
+        {
+            "Effect": "Allow",
+            "Principal": {
+                "Federated": "arn:aws:iam::<ACCOUNT_ID>:saml-provider/Okta"
+            },
+            "Action": "sts:AssumeRoleWithSAML",
+            "Condition": {
+                "StringEquals": {
+                    "SAML:aud": "https://signin.aws.amazon.com/saml"
+                }
+            }
+        },
+        {
+            "Sid": "",
+            "Effect": "Allow",
+            "Principal": {
+                "Service": "sagemaker.amazonaws.com"
+            },
+            "Action": "sts:AssumeRole"
+        }
+    ]
+}
+```
diff --git a/examples/sagemaker_example/archetype/liminal.yaml b/examples/sagemaker_example/archetype/liminal.yaml
new file mode 100644
index 0000000..48e4b42
--- /dev/null
+++ b/examples/sagemaker_example/archetype/liminal.yaml
@@ -0,0 +1,57 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+---
+
+# superliminal for local development
+name: InfraSageMaker
+owner: Bosco Albert Baracus
+type: super
+secrets:
+  - secret: aws
+    local_path_file: "~/.aws/credentials"
+executors:
+  - executor: k8s
+    type: kubernetes
+variables:
+  output_root_dir: "/mnt/smvolume"
+  input_root_dir: ''
+images:
+  - image: my_sm_image
+    source: .
+    type: python
+    no_cache: false
+task_defaults:
+  python:
+    executor: k8s
+    image: my_sm_image
+    application_source: '{{application}}'
+    env_vars:
+      AWS_CONFIG_FILE: "/secret/credentials"
+      COMM_PATH: "{{output_root_dir}}"
+    secrets:
+      - secret: aws
+        remote_path: "/secret"
+    mounts:
+      - mount: mymount
+        volume: smvolume
+        path: /mnt/smvolume
+volumes:
+  - volume: smvolume
+    claim_name: smvolume-pvc
+    local:
+      path: .
diff --git a/examples/sagemaker_example/data_preparation/__init__.py b/examples/sagemaker_example/data_preparation/__init__.py
new file mode 100644
index 0000000..217e5db
--- /dev/null
+++ b/examples/sagemaker_example/data_preparation/__init__.py
@@ -0,0 +1,17 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
diff --git a/examples/sagemaker_example/data_preparation/data_preparation.py b/examples/sagemaker_example/data_preparation/data_preparation.py
new file mode 100644
index 0000000..80e1af7
--- /dev/null
+++ b/examples/sagemaker_example/data_preparation/data_preparation.py
@@ -0,0 +1,77 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import argparse
+import os
+from pathlib import Path
+
+import pandas as pd
+from data_preparation.data_uploader import get_uploader
+from sklearn.model_selection import train_test_split
+
+TEST_CSV = "diamonds_test.csv"
+
+TRAIN_CSV = "diamonds_train.csv"
+
+LABEL_COLUMN = 'price'
+
+DATASET_PUBLIC_URL = "https://www.openml.org/data/get_csv/21792853/dataset"
+
+
+def transform(data):
+    X = data.drop(columns=LABEL_COLUMN)
+    y = data[LABEL_COLUMN]
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
+    train = X_train.copy()
+    train[LABEL_COLUMN] = y_train
+
+    test = X_test.copy()
+    test[LABEL_COLUMN] = y_test
+    return train, test
+
+
+def extract(input_uri):
+    return pd.read_csv(input_uri)
+
+
+def load(train, test, output_uri_base, data_uploader):
+    train.to_csv(TRAIN_CSV)
+    test.to_csv(TEST_CSV)
+    train_path = data_uploader.upload(TRAIN_CSV, os.path.join(output_uri_base, "train"))
+    test_path = data_uploader.upload(TEST_CSV, os.path.join(output_uri_base, "test"))
+    return train_path, test_path
+
+
+def data_pipeline(input_uri, output_uri_base, data_uploader):
+    data = extract(input_uri)
+    train, test = transform(data)
+    return load(train, test, output_uri_base, data_uploader)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input_uri", default=DATASET_PUBLIC_URL)
+    parser.add_argument(
+        "--output_uri_base",
+        default=f"file://{Path(__file__).parent.parent.absolute().joinpath('data')}",
+        help="a uri starting with 's3', 'file' or a relative path " "which will be treated as sagemaker prefix",
+    )
+    args = parser.parse_args()
+    data_uploader = get_uploader(args.output_uri_base)
+    data_pipeline(args.input_uri, args.output_uri_base, data_uploader)
diff --git a/examples/sagemaker_example/data_preparation/data_uploader.py b/examples/sagemaker_example/data_preparation/data_uploader.py
new file mode 100644
index 0000000..12a50d1
--- /dev/null
+++ b/examples/sagemaker_example/data_preparation/data_uploader.py
@@ -0,0 +1,82 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import os
+import shutil
+from abc import ABC, abstractmethod
+from urllib.parse import urlparse
+
+import boto3
+import sagemaker
+
+
+def get_uploader(output_uri_base):
+    o = urlparse(output_uri_base)
+    if o.scheme == 's3':
+        return S3DataUploader()
+    elif o.scheme == "file":
+        return LocalDataUploader()
+    return SagemakerDataUploader()
+
+
+class DataUploader(ABC):
+    def __init__(self):
+        pass
+
+    @abstractmethod
+    def upload(self, local_path, output_uri_base):
+        pass
+
+
+class LocalDataUploader(DataUploader):
+    s3_client = None
+
+    def upload(self, local_path, output_uri_base):
+        o = urlparse(output_uri_base)
+        os.makedirs(o.path, exist_ok=True)
+        shutil.copy2(local_path, o.path)
+        return o.path
+
+
+class S3DataUploader(DataUploader):
+    s3_client = None
+
+    def __init__(self):
+        self.s3_client = boto3.client('s3')
+
+    def upload(self, local_path, output_uri_base):
+        o = urlparse(output_uri_base)
+        response = self.s3_client.upload_file(local_path, o.netloc, o.path)
+
+
+class SagemakerDataUploader(DataUploader):
+    sm_client = None
+    sm_session = None
+    region = None
+    default_bucket = None
+
+    def __init__(self):
+        self.sm_client = boto3.client("sagemaker")
+        self.sm_session = sagemaker.Session()
+        self.region = self.sm_session.boto_session.region_name
+        self.default_bucket = self.sm_session.default_bucket()
+
+    def upload(self, local_path, output_uri_base):
+        o = urlparse(output_uri_base)
+        bucket = o.netloc or self.default_bucket
+        return self.sm_session.upload_data(path=local_path, bucket=bucket, key_prefix=o.path)
diff --git a/examples/sagemaker_example/data_train/__init__.py b/examples/sagemaker_example/data_train/__init__.py
new file mode 100644
index 0000000..217e5db
--- /dev/null
+++ b/examples/sagemaker_example/data_train/__init__.py
@@ -0,0 +1,17 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
diff --git a/examples/sagemaker_example/data_train/train.py b/examples/sagemaker_example/data_train/train.py
new file mode 100644
index 0000000..f665625
--- /dev/null
+++ b/examples/sagemaker_example/data_train/train.py
@@ -0,0 +1,94 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import argparse
+import os
+from pathlib import Path
+
+import joblib
+import numpy as np
+import pandas as pd
+from data_preparation.data_preparation import LABEL_COLUMN, TEST_CSV, TRAIN_CSV
+from sklearn.compose import ColumnTransformer
+from sklearn.linear_model import LinearRegression
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import OneHotEncoder
+
+feature_column_names = ['carat', 'cut', 'color', 'clarity', 'depth', 'table', 'x', 'y', 'z']
+
+
+categorical_cols = ['cut', 'clarity', 'color']
+
+# inference functions
+MODEL_JOBLIB_FILENAME = "model.joblib"
+
+preprocessor = ColumnTransformer(
+    transformers=[('categorical', OneHotEncoder(), categorical_cols)], remainder='passthrough'
+)
+
+
+def train(train_df, test_df, n_jobs, model_dir):
+    X_train = train_df[feature_column_names]
+    y_train = train_df[LABEL_COLUMN]
+
+    print(X_train.head(3))
+    X_test = test_df[feature_column_names]
+    y_test = test_df[LABEL_COLUMN]
+
+    # train
+
+    diamond_price_model = LinearRegression(n_jobs=n_jobs)
+
+    my_pipeline = Pipeline(steps=[('preprocessor', preprocessor), ('model', diamond_price_model)])
+
+    my_pipeline.fit(X_train, y_train)
+
+    train_predictions = my_pipeline.predict(X_train)
+    print(train_predictions)
+
+    print("validating model")
+    abs_err = np.abs(my_pipeline.predict(X_test) - y_test)
+    print(f"Test prediction error:{abs_err} ")
+
+    # persist model
+    path = os.path.join(model_dir, MODEL_JOBLIB_FILENAME)
+    joblib.dump(my_pipeline, path)
+    print("model persisted at " + path)
+    return path
+
+
+if __name__ == '__main__':
+    data_path = Path(__file__).parent.parent.absolute().joinpath('data')
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model-dir', type=str, default=os.getenv('SM_MODEL_DIR', f"file://{data_path}"))
+    parser.add_argument('--n-jobs', default=2)
+    parser.add_argument(
+        "--train", type=str, default=os.getenv("SM_CHANNEL_TRAIN", f"file://{data_path.joinpath('train')}")
+    )
+    parser.add_argument(
+        "--test", type=str, default=os.getenv("SM_CHANNEL_TEST", f"file://{data_path.joinpath('test')}")
+    )
+    parser.add_argument("--train-file", type=str, default=TRAIN_CSV)
+    parser.add_argument("--test-file", type=str, default=TEST_CSV)
+
+    args = parser.parse_args()
+
+    train_df = pd.read_csv(os.path.join(args.train, args.train_file))
+    test_df = pd.read_csv(os.path.join(args.test, args.test_file))
+
+    train(train_df=train_df, test_df=test_df, n_jobs=args.n_jobs, model_dir=args.model_dir)
diff --git a/examples/sagemaker_example/inference.py b/examples/sagemaker_example/inference.py
new file mode 100644
index 0000000..2244598
--- /dev/null
+++ b/examples/sagemaker_example/inference.py
@@ -0,0 +1,76 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import argparse
+import os
+from io import BytesIO
+
+import joblib
+import numpy as np
+import pandas as pd
+
+feature_column_names = ['carat', 'cut', 'color', 'clarity', 'depth', 'table', 'x', 'y', 'z']
+
+
+categorical_cols = ['cut', 'clarity', 'color']
+
+MODEL_JOBLIB_FILENAME = "model.joblib"
+
+
+def model_fn(model_dir):
+    clf = joblib.load(os.path.join(model_dir, MODEL_JOBLIB_FILENAME))
+    return clf
+
+
+def input_fn(input_data, content_type):
+    if content_type == "application/x-npy":
+        load_bytes = BytesIO(input_data)
+        input_np = np.load(load_bytes, allow_pickle=True)
+        df = pd.DataFrame(data=input_np, columns=feature_column_names)
+        return df
+    else:
+        raise ValueError(
+            f"content type {content_type} is not supported by this inference endpoint. Please send a legal application/x-npy payload"
+        )
+
+
+def predict_fn(input_data, model):
+    prediction = model.predict(input_data[feature_column_names])
+    return prediction
+
+
+def df_to_inference_input():
+    X_train = df[feature_column_names]
+    rows = X_train.head(10)
+    inference_input = rows.to_numpy()
+    np_bytes = BytesIO()
+    np.save(np_bytes, inference_input, allow_pickle=True)
+    input_data = input_fn(np_bytes.getvalue(), "application/x-npy")
+    return input_data
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-dir", type=str, default=os.getenv("SM_MODEL_DIR", "../data"))
+    parser.add_argument("--data-path", type=str, default=f"../data/test/diamonds_test.csv")
+    args = parser.parse_args()
+    model = model_fn(args.model_dir)
+    df = pd.read_csv(args.data_path)
+    input_data = df_to_inference_input()
+    predictions = predict_fn(input_data, model)
+    print(predictions)
diff --git a/examples/sagemaker_example/liminal.yaml b/examples/sagemaker_example/liminal.yaml
new file mode 100644
index 0000000..9ea8adb
--- /dev/null
+++ b/examples/sagemaker_example/liminal.yaml
@@ -0,0 +1,43 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+---
+name: SageMakerExample
+super: InfraSageMaker
+owner: Bosco Albert Baracus
+pipelines:
+  - pipeline: sagemaker_diamonds_train_and_inference
+    start_date: 1970-01-01
+    timeout_minutes: 45
+    schedule: 0 * 1 * *
+    tasks:
+      - task: data_preprocessing
+        type: python
+        description: prepare the data for training
+        cmd: python -u liminal_sm.py --action data_prep --output_uri_base liminal-sm-example/data
+      - task: train
+        type: python
+        description: train model
+        cmd: python -u liminal_sm.py --action train --base_job_name liminal-base-training-job --train_instance_type ml.m5.large --n_jobs 5
+      - task: deploy
+        type: python
+        description: Deploy model
+        cmd: python -u liminal_sm.py --action deploy --model_name liminal-sm-diamonds-model --deploy_instance_type ml.m5.large
+      - task: validate
+        type: python
+        description: validate model post deployment
+        cmd: python -u liminal_sm.py --action validate
diff --git a/examples/sagemaker_example/liminal_sm.py b/examples/sagemaker_example/liminal_sm.py
new file mode 100644
index 0000000..a02719f
--- /dev/null
+++ b/examples/sagemaker_example/liminal_sm.py
@@ -0,0 +1,81 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import os
+import tempfile
+
+from sagemaker.deserializers import NumpyDeserializer
+from sagemaker.predictor import Predictor
+from sagemaker.serializers import NumpySerializer
+from sm_ops import create_sm_args_parser, sm_data_prep, sm_deploy, sm_train, sm_validate
+
+COMM_PATH = os.getenv("COMM_PATH", tempfile.mkdtemp())
+os.makedirs(COMM_PATH, exist_ok=True)
+
+
+def read_message(name):
+    with open(os.path.join(COMM_PATH, name)) as f:
+        lines = f.readlines()
+    return [x.strip() for x in lines]
+
+
+def forward_message(name, lines):
+    if not isinstance(lines, list):
+        lines = [lines]
+    lines = '\n'.join(lines) + '\n'
+    with open(os.path.join(COMM_PATH, name), 'w') as f:
+        f.writelines(lines)
+
+
+if __name__ == '__main__':
+    choices = ['data_prep', 'train', 'deploy', 'validate', 'all']
+    parser = create_sm_args_parser()
+    parser.add_argument("--action", choices=choices, default='all')
+    args = parser.parse_args()
+    steps = [args.action]
+    if args.action == 'all':
+        steps = choices[:-1]
+    for step in steps:
+        if step == 'data_prep':
+            train, test = sm_data_prep(args.input_uri, args.output_uri_base)
+            forward_message('data_prep', [train, test])
+        elif step == 'train':
+            lines = read_message('data_prep')
+            train, test = lines[0], lines[1]
+            artifact = sm_train(
+                train,
+                test,
+                base_job_name=args.base_job_name,
+                instance_type=args.train_instance_type,
+                n_jobs=args.n_jobs,
+            )
+            forward_message('train', artifact)
+        elif step == 'deploy':
+            artifact = read_message('train')[0]
+            predictor = sm_deploy(
+                artifact=artifact, model_name=args.model_name, instance_type=args.deploy_instance_type
+            )
+            forward_message('deploy', predictor.endpoint)
+        else:
+            endpoint = read_message('deploy')[0]
+            train, test = read_message('data_prep')
+            predictor = Predictor(
+                endpoint_name=endpoint, serializer=NumpySerializer(), deserializer=NumpyDeserializer()
+            )
+            result = sm_validate(predictor, test)
+            print(f"avg abs error:{result}")
diff --git a/examples/sagemaker_example/requirements.txt b/examples/sagemaker_example/requirements.txt
new file mode 100644
index 0000000..6f82102
--- /dev/null
+++ b/examples/sagemaker_example/requirements.txt
@@ -0,0 +1,22 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+sagemaker==2.57.0
+scikit-learn==0.23.2
+s3fs==2022.5.0
+protobuf==3.20.*
diff --git a/examples/sagemaker_example/sm_ops.py b/examples/sagemaker_example/sm_ops.py
new file mode 100644
index 0000000..0c9b30c
--- /dev/null
+++ b/examples/sagemaker_example/sm_ops.py
@@ -0,0 +1,140 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import argparse
+import os
+import time
+
+import boto3
+import numpy as np
+import pandas as pd
+import sagemaker
+from data_preparation.data_preparation import (
+    DATASET_PUBLIC_URL,
+    LABEL_COLUMN,
+    data_pipeline,
+)
+from data_preparation.data_uploader import SagemakerDataUploader
+from data_train.train import feature_column_names
+from sagemaker.sklearn.estimator import SKLearn
+from sagemaker.sklearn.model import SKLearnModel
+
+FRAMEWORK_VERSION = "0.23-1"
+
+sm_boto3 = boto3.client("sagemaker")
+
+
+def get_role():
+    try:
+        role = sagemaker.get_execution_role()
+    except ValueError:
+        role = os.getenv("SM_ROLE")
+    return role
+
+
+def sm_train(train_path, test_path, base_job_name="Liminal-sm-training-job", instance_type="ml.m5.large", n_jobs=1):
+    sklearn_estimator = SKLearn(
+        entry_point="data_train/train.py",
+        source_dir="./",
+        role=get_role(),
+        instance_count=1,
+        instance_type=instance_type,
+        framework_version=FRAMEWORK_VERSION,
+        base_job_name=base_job_name,
+        hyperparameters={
+            "n-jobs": n_jobs,
+        },
+    )
+    sklearn_estimator.fit({"train": train_path, "test": test_path})
+    artifact = sm_boto3.describe_training_job(TrainingJobName=sklearn_estimator.latest_training_job.name)[
+        "ModelArtifacts"
+    ]["S3ModelArtifacts"]
+
+    print("Model artifact persisted at " + artifact)
+    return artifact
+
+
+def sm_deploy(artifact, model_name="Liminal-sm-demo-model", instance_type="ml.m5.large"):
+
+    timestamp = time.strftime("-%Y-%m-%d-%H-%M-%S", time.gmtime())
+    model = SKLearnModel(
+        name=f"{model_name}-{timestamp}",
+        entry_point="inference.py",
+        model_data=artifact,
+        role=get_role(),
+        framework_version=FRAMEWORK_VERSION,
+    )
+
+    predictor = model.deploy(instance_type=instance_type, initial_instance_count=1, wait=True)
+    return predictor
+
+
+def sm_validate(predictor, test_path):
+    if not test_path.startswith("s3"):
+        test_path = f"s3://{test_path}"
+
+    df = pd.read_csv(test_path)
+    X_test_inference = df[feature_column_names].to_numpy()
+    predictions = predictor.predict(data=X_test_inference)
+    print("validating model")
+    avg_err = np.average(np.abs(predictions - df[LABEL_COLUMN]))
+    print(f"average abs error:{avg_err}")
+    print(predictions)
+    return avg_err
+
+
+def sm_data_prep(input_uri, output_uri_base):
+    return data_pipeline(
+        input_uri=input_uri or DATASET_PUBLIC_URL,
+        output_uri_base=output_uri_base,
+        data_uploader=SagemakerDataUploader(),
+    )
+
+
+def sm_main(args):
+    test_path = 'sagemaker-us-east-1-468326661668/liminal-sm-example/test/diamonds_test.csv'
+    train_path, test_path = sm_data_prep(args.input_uri, args.output_uri_base)
+    artifact = sm_train(
+        train_path,
+        test_path,
+        base_job_name=args.base_job_name,
+        instance_type=args.train_instance_type,
+        n_jobs=args.n_jobs,
+    )
+    # artifact = 's3://sagemaker-us-east-1-468326661668/Liminal-sm-training-job-2022-06-06-14-30-39-827/output/model.tar.gz'
+    predictor = sm_deploy(artifact, model_name=args.model_name, instance_type=args.deploy_instance_type)
+    # predictor =  Predictor(endpoint_name="Liminal-sm-demo-model-2022-06-06-14-35-48-079", serializer=NumpySerializer(), deserializer=NumpyDeserializer())
+    sm_validate(predictor, test_path)
+
+
+def create_sm_args_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input_uri", default=DATASET_PUBLIC_URL)
+    parser.add_argument("--output_uri_base", default="liminal-sm-example")
+    parser.add_argument("--base_job_name", default="Liminal-sm-training-job")
+    parser.add_argument("--train_instance_type", default="ml.m5.large")
+    parser.add_argument("--n_jobs", default=1)
+    parser.add_argument("--model_name", default="Liminal-sm-demo-model")
+    parser.add_argument("--deploy_instance_type", default="ml.m5.large")
+    return parser
+
+
+if __name__ == '__main__':
+    parser = create_sm_args_parser()
+    args = parser.parse_args()
+    sm_main(args)
diff --git a/liminal/build/image/python/Dockerfile b/liminal/build/image/python/Dockerfile
index 67d8462..9d0be8f 100644
--- a/liminal/build/image/python/Dockerfile
+++ b/liminal/build/image/python/Dockerfile
@@ -20,7 +20,9 @@
 FROM {{python}}
 
 # Install aptitude build-essential
-#RUN apt-get install -y --reinstall build-essential
+RUN apt-get update && apt-get install -y --reinstall build-essential \
+                                                   make \
+                                                   gcc
 
 # Set the working directory to /app
 WORKDIR /app
@@ -32,7 +34,7 @@
 COPY ./requirements.txt /app/
 
 # mount the secret in the correct location, then run pip install
-RUN pip install --upgrade pip
+RUN python -m pip install --upgrade pip
 RUN {{mount}} pip install -r requirements.txt
 
 # Copy the current directory contents into the container at /app
diff --git a/liminal/kubernetes/secret_util.py b/liminal/kubernetes/secret_util.py
index e5bccc8..f9e4c69 100644
--- a/liminal/kubernetes/secret_util.py
+++ b/liminal/kubernetes/secret_util.py
@@ -63,6 +63,11 @@
     _LOG.info(f'Requested secret {name}')
 
     if name not in _LOCAL_VOLUMES:
+        matching_secrets = _kubernetes.list_namespaced_secret(
+            namespace, field_selector=f'metadata.name={name}'
+        ).to_dict()['items']
+
+    if len(matching_secrets) == 0:
         _create_secret(namespace, conf, name)
         sleep(5)
 
diff --git a/liminal/runners/airflow/executors/kubernetes.py b/liminal/runners/airflow/executors/kubernetes.py
index 409e603..b69babe 100644
--- a/liminal/runners/airflow/executors/kubernetes.py
+++ b/liminal/runners/airflow/executors/kubernetes.py
@@ -85,9 +85,6 @@
 
     def __kubernetes_kwargs(self, task: ContainerTask):
         config = copy.deepcopy(self.executor_config)
-        for secret in task.secrets:
-            result = next(x for x in self.liminal_config['secrets'] if x['secret'] == secret['secret'])
-            task.mounts.append({'volume': result['secret'], 'path': result['remote_path']})
 
         kubernetes_kwargs = {
             'task_id': task.task_id,
@@ -115,6 +112,15 @@
                     read_only=mount.get('read_only', False),
                 )
                 for mount in task.mounts
+            ]
+            + [
+                V1VolumeMount(
+                    name=secret['secret'],
+                    mount_path=secret['remote_path'],
+                    sub_path=secret.get('sub_path'),
+                    read_only=secret.get('read_only', False),
+                )
+                for secret in task.secrets
             ],
         }
 
diff --git a/scripts/docker-compose.yml b/scripts/docker-compose.yml
index d86ef2e..e61396a 100644
--- a/scripts/docker-compose.yml
+++ b/scripts/docker-compose.yml
@@ -28,6 +28,7 @@
     LOAD_EX: n
     AIRFLOW__CORE__EXECUTOR: LocalExecutor
     KUBECONFIG: /home/airflow/kube/config
+    AWS_DEFAULT_REGION: "${AWS_DEFAULT_REGION:-us-east-1}"
     AIRFLOW__CORE__LOAD_EXAMPLES: 'False'
     AIRFLOW__WEBSERVER__WORKERS: '1'
     AIRFLOW__WEBSERVER__EXPOSE_CONFIG: 'True'
diff --git a/scripts/liminal b/scripts/liminal
index 897796c..51354f9 100755
--- a/scripts/liminal
+++ b/scripts/liminal
@@ -159,12 +159,13 @@
         pathlib.Path(target_yml_name).parent.mkdir(parents=True, exist_ok=True)
         shutil.copyfile(config_file, target_yml_name)
         configs_path = ConfigUtil(os.path.dirname(config_file)).safe_load(is_render_variables=True,
-                                            soft_merge=True)
+                                                                          soft_merge=True)
         for config in configs_path:
             if config.get('secrets'):
                 logging.info(f'Secret volume is being created')
                 secret_util.create_local_secrets(config)
 
+
 def is_file_empty(file):
     try:
         with open(file, "r") as stream:
@@ -173,6 +174,7 @@
         print(exc)
     return data_loaded is None
 
+
 def liminal_is_running():
     stdout, stderr = docker_compose_command('ps', [])
     return "liminal" in stdout
diff --git a/tests/runners/airflow/liminal/liminal.yml b/tests/runners/airflow/liminal/liminal.yml
index a54a7bd..170701b 100644
--- a/tests/runners/airflow/liminal/liminal.yml
+++ b/tests/runners/airflow/liminal/liminal.yml
@@ -23,7 +23,6 @@
       path: /tmp/liminal_tests
 secrets:
   - secret: aws
-    remote_path: "/mnt"
     local_path_file: "~/.aws/credentials"
 images:
   - image: my_python_task_img
@@ -68,6 +67,7 @@
           AWS_PROFILE: "dev"
         secrets:
           - secret: aws
+            remote_path: "/mnt"
         mounts:
           - mount: mymount
             volume: myvol1
diff --git a/tests/runners/airflow/tasks/test_python.py b/tests/runners/airflow/tasks/test_python.py
index bb9dd2a..019b191 100644
--- a/tests/runners/airflow/tasks/test_python.py
+++ b/tests/runners/airflow/tasks/test_python.py
@@ -148,7 +148,7 @@
             'image': image,
             'env_vars': env_vars if env_vars is not None else {},
             'mounts': [{'mount': 'mymount', 'volume': self._VOLUME_NAME, 'path': '/mnt/vol1'}],
-            'secrets': [{'secret': self._SECRET_NAME}],
+            'secrets': [{'secret': self._SECRET_NAME, 'remote_path': '/mnt'}],
         }
 
         if executors:
diff --git a/yamllint-config.yml b/yamllint-config.yml
index ea52231..d93684d 100644
--- a/yamllint-config.yml
+++ b/yamllint-config.yml
@@ -28,7 +28,7 @@
 
 rules:
   line-length:
-    max: 110
+    max: 140
     # level: warning
   brackets: enable
   colons: enable