Merge branch 'main' into jsonlines6

LICENSE.txt

@@ -200,3 +200,21 @@
    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.
+
+   ======================================================================================
+    Amazon SageMaker Examples Subcomponents:
+
+    The Amazon SageMaker Examples project contains subcomponents with separate
+    copyright notices and license terms. Your use of the source code for the
+    these subcomponents is subject to the terms and conditions of the following
+    licenses. See licenses/ for text of these licenses.
+
+    If a folder hierarchy is listed as subcomponent, separate listings of
+    further subcomponents (files or folder hierarchies) part of the hierarchy
+    take precedence.
+
+    =======================================================================================
+    2-clause BSD license
+    =======================================================================================
+    _static/kendrasearchtools.js
+    _templates/search.html

README.md

@@ -54,6 +54,7 @@ These examples provide a gentle introduction to machine learning concepts as the
 - [Population Segmentation of US Census Data using PCA and Kmeans](introduction_to_applying_machine_learning/US-census_population_segmentation_PCA_Kmeans) analyzes US census data and reduces dimensionality using PCA then clusters US counties using KMeans to identify segments of similar counties.
 - [Document Embedding using Object2Vec](introduction_to_applying_machine_learning/object2vec_document_embedding) is an example to embed a large collection of documents in a common low-dimensional space, so that the semantic distances between these documents are preserved.
 - [Traffic violations forecasting using DeepAR](introduction_to_applying_machine_learning/deepar_chicago_traffic_violations) is an example to use daily traffic violation data to predict pattern and seasonality to use Amazon DeepAR alogorithm.
+- [Visual Inspection Automation with Pre-trained Amazon SageMaker Models](introduction_to_applying_machine_learning/visual_object_detection) is an example for fine-tuning pre-trained Amazon Sagemaker models on a target dataset.
 
 ### SageMaker Automatic Model Tuning
 
@@ -75,6 +76,7 @@ These examples introduce SageMaker Autopilot. Autopilot automatically performs f
 - [Customer Churn AutoML](autopilot/) shows how to use SageMaker Autopilot to automatically train a model for the [Predicting Customer Churn](introduction_to_applying_machine_learning/xgboost_customer_churn) task.
 - [Targeted Direct Marketing AutoML](autopilot/) shows how to use SageMaker Autopilot to automatically train a model.
 - [Housing Prices AutoML](sagemaker-autopilot/housing_prices) shows how to use SageMaker Autopilot for a linear regression problem (predict housing prices).
+- [Portfolio Churn Prediction with Amazon SageMaker Autopilot and Neo4j](autopilot/sagemaker_autopilot_neo4j_portfolio_churn.ipynb) shows how to use SageMaker Autopilot with graph embeddings to predict investment portfolio churn.
 
 ### Introduction to Amazon Algorithms
 
@@ -185,6 +187,7 @@ These examples showcase unique functionality available in Amazon SageMaker. They
 - [Host Multiple Models with SKLearn](advanced_functionality/multi_model_sklearn_home_value) shows how to deploy multiple models to a realtime hosted endpoint using a multi-model enabled SKLearn container.
 - [SageMaker Training and Inference with Script Mode](sagemaker-script-mode) shows how to use custom training and inference scripts, similar to those you would use outside of SageMaker, with SageMaker's prebuilt containers for various frameworks like Scikit-learn, PyTorch, and XGBoost.
 - [Host Models with NVidia Triton Server](sagemaker-triton) shows how to deploy models to a realtime hosted endpoint using [Triton](https://developer.nvidia.com/nvidia-triton-inference-server) as the model inference server.
+- [Heterogenous Clusters Training in TensorFlow or PyTorch ](training/heterogeneous-clusters/README.md) shows how to train using TensorFlow tf.data.service (distributed data pipeline) or Pytorch (with gRPC) on top of Amazon SageMaker Heterogenous clusters to overcome CPU bottlenecks by including different instance types (GPU/CPU) in the same training job.
 
 ### Amazon SageMaker Neo Compilation Jobs