Releasing version

Author: rnahar25

README.md

@@ -1,45 +1,36 @@
-Oracle Cloud Infrastructure Data Science and AI services Examples  
+Oracle Cloud Infrastructure Data Science and AI services Examples
 =================================================================
 
-The Oracle Cloud Infrastructure (OCI) Data Science product management team is maintaining a series of demos, tutorials, and code examples highlighting the different features of both [OCI Data Science](https://www.oracle.com/data-science/cloud-infrastructure-data-science.html) and AI services. 
+The Oracle Cloud Infrastructure (OCI) Data Science service has created this repo to make demos, tutorials, and code examples that highlight various features of the [OCI Data Science service](https://www.oracle.com/data-science/cloud-infrastructure-data-science.html) and AI services. We welcome your feedback and would like to know what content is useful and what content is missing. Open an [issue](https://github.com/oracle/oci-data-science-ai-samples/issues) to do this. We know that a lot of you are creating great content and we would like to help you share it. See the [contributions](CONTRIBUTING.md) document.
 
-Each directory in this repo corresponds to a different demo/lab and contains its own separate README file giving you instructions on how to run the code examples. 
+# Sections
 
-## Table of Examples 
+* [ads_notebooks](./ads_notebooks/): The [Accelerated Data Science (ADS) SDK](https://docs.oracle.com/en-us/iaas/tools/ads-sdk/latest/index.html) is a data scientist friendly library that helps you speed up common data science tasks and it also provides an interface to other OCI services. This section contains JupyterLab notebooks that provide tutorials on how to use ADS. For example, the [vault.ipynb](./ads_notebooks/vault.ipynb) shows how easy it is to store you secrets in the [OCI Vault service](https://docs.oracle.com/en-us/iaas/Content/KeyManagement/Concepts/keyoverview.htm).
+* [conda_environment_notebooks](./conda_environment_notebooks/): The [OCI Data Science service](https://www.oracle.com/data-science/cloud-infrastructure-data-science.html) uses [conda environments](https://docs.conda.io/projects/conda/en/latest/index.html) to manage the available libraries that a notebook can use. OCI The Data Science service [provides a number of conda environments](https://docs.oracle.com/en-us/iaas/data-science/using/conda_understand_environments.htm) that are designed to give you the best in class libraries for common data science tasks. Each family of conda environments has notebooks that demonstrate how to perform different data science tasks. This section is organized around these conda environment families and provides the notebooks that you need to get you started quickly.
+* [knowledge_base](./knowledge_base/): Are you struggling with a problem? Check out the knowledge base. It has a growing section of articles on how to solve common problems that you may encounter. If you have had a problem and have solved it, please consider [contributing](./CONTRIBUTING.md) your solution. If you had a problem, others probably have had the same one.
+* [labs](./labs/): Looking to walk through an end-to-end problem? Check out this section. It has examples of how to train machine learning models and then deploy them on the OCI Data Science service. Have you built an end-to-end machine learning model and want to share it with others. Please consider [contributing](./CONTRIBUTING.md) it.
+* [model_catalog_examples](model_catalog_examples/): The [model catalog](https://docs.oracle.com/en-us/iaas/tools/ads-sdk/latest/user_guide/modelcatalog/modelcatalog.html) provides a managed and centralized storage space for models. ADS helps you create the artifacts that you need to use this service. However, you need to provide a [`score.py`](https://docs.oracle.com/en-us/iaas/data-science/using/model_score_py.htm) file that will load the model and a function that will make predictions. The [`runtime.yaml`](https://docs.oracle.com/en-us/iaas/data-science/using/model_runtime_yaml.htm) provides information about the runtime conda environment if you want to deploy the model. It also allows you to document a comprehensive set of metadata about the provenance of the model. The section of the repo provides examples of how to create your `score.py` and `runtime.yaml` files for various common machine learning models. There are many different models and configurations. If you have developed a machine learning model that is not in this section, please consider [contributing](./CONTRIBUTING.md) it.
 
-* [labs](./labs/) 
-  In this directory we have 4 different examples to do things like end-to-end build, train, deploy and invoke a machine learning model using OCI Data Science services.
-* [ads_v_2_2_0](./ads_v_2_2_0/)
-  This directory has all of our generic ADS notebook examples. Here, for example, you can find a notebook about vault connection and model deployment.
-* [environment_examples](./environment_examples/)
-  This directory has subdirectories for the different conda environments offered. Within each subdirectory you will find notebook examples on that topic.
-* [model_catalog_examples](model_catalog_examples/)
-  Here we have several different examples of model catalogs, we have offered sample runtime.yaml and score.py files on several different topics that can be used to deploy models.
-* [knowledge_base](./knowledge_base/)
-  Directory with tips and tricks on a variety of topics.
+# Resources
 
-## Documentation 
+Check out the following resources for more information about the OCI Data Science and AI services:
 
-There are several ways you can access OCI Data Science documentation: 
-* our official OCI Data Science [service documentation site](https://docs.oracle.com/en-us/iaas/data-science/using/data-science.htm)
-* our [YouTube playlist](https://www.youtube.com/playlist?list=PLKCk3OyNwIzv6CWMhvqSB_8MLJIZdO80L)
-* our [AI & Data Science blog](https://blogs.oracle.com/ai-and-datascience/) 
+* [ADS class documentation](https://docs.oracle.com/en-us/iaas/tools/ads-sdk/latest/modules.html)
+* [ADS user guide](https://docs.oracle.com/en-us/iaas/tools/ads-sdk/latest/index.html)
+* [AI & Data Science blog](https://blogs.oracle.com/ai-and-datascience/)
+* [OCI Data Science service guide](https://docs.oracle.com/en-us/iaas/data-science/using/data-science.htm)
+* [OCI Data Science service release notes](https://docs.cloud.oracle.com/en-us/iaas/releasenotes/services/data-science/)
+* [YouTube playlist](https://www.youtube.com/playlist?list=PLKCk3OyNwIzv6CWMhvqSB_8MLJIZdO80L)
 
-If you want to know more about Oracle Accelerated Data Science (ADS) Python SDK, please visit our [ADS user documentation website](https://docs.oracle.com/en-us/iaas/tools/ads-sdk/latest/index.html).   
-## Need Help? 
+# Need Help?
 
-* Create a github [issue](https://github.com/oracle/oci-data-science-ai-samples/issues). 
+* Create a GitHub [issue](https://github.com/oracle/oci-data-science-ai-samples/issues).
 
-## Contributing
+# Contributing
 
 This project welcomes contributions from the community. Before submitting a pull request, please [review our contribution guide](./CONTRIBUTING.md).
 
 ## Security
 
-Please consult the [security guide](./SECURITY.md) for our responsible security vulnerability disclosure process.
+The [Security Guide](./SECURITY.md) contains information about security vulnerability disclosure process. If you discover a vulnerability, consider filing an [issue](https://github.com/oracle/oci-data-science-ai-samples/issues).
 
-## License
-
-Copyright (c) 2021 Oracle and/or its affiliates.
-
-Released under the Universal Permissive License v1.0 as shown at <https://oss.oracle.com/licenses/upl/>.

ads_notebooks/README.md

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+ADS Notebooks
+=============
+
+The [Accelerated Data Science (ADS) SDK](https://docs.oracle.com/en-us/iaas/tools/ads-sdk/latest/index.html) is a data scientist friendly library that helps you speed up common data science tasks and it also provides an interface to other Oracle Cloud Infrastructure (OCI) services. This section contains JupyterLab notebooks that provide tutorials on how to use ADS. For example, the [vault.ipynb](./ads_notebooks/vault.ipynb) notebook shows how easy it is to store you secrets in the [OCI Vault service](https://docs.oracle.com/en-us/iaas/Content/KeyManagement/Concepts/keyoverview.htm).
+
+# Notebook Descriptions
+
+* [api_keys.ipynb](api_keys.ipynb): A notebook session runs as a user that has no permissions in your tenancy. To access other OCI resources the notebook must be given permission. One way to do this is to register a set of API keys with your account and then provide the private key to your notebook session so that it can authenticate as you. This notebook demonstrates how to do this.
+* [model_catalog.ipynb](model_catalog.ipynb): The OCI Data Science service offers a model catalog. It provides centralized storage space to manage your machine learning models. Entities in the model catalog are immutable. They can be shared with others and quickly deployed with the [model deployment](https://docs.content.oci.oracleiaas.com/en-us/iaas/data-science/using/model-dep-about.htm) service. This notebook demonstrates how to create a model and the necessary model artifacts so that it can be registered with the model catalog.
+* [model_deployment.ipynb](model_deployment.ipynb): Once you have created your model it needs to be deployed to production. This is often a challenging task for data scientists. With the [model deployment](https://docs.content.oci.oracleiaas.com/en-us/iaas/data-science/using/model-dep-about.htm) service this task is simple to perform. The notebook walks you through the steps to create an API endpoint that you can use to make predictions.
+* [model_deployment_fn.ipynb](model_deployment_fn.ipynb): While the preferred method to deploy models is the [model deployment](https://docs.content.oci.oracleiaas.com/en-us/iaas/data-science/using/model-dep-about.htm) service, there may be instances when you want to use [Functions](https://docs.oracle.com/en-us/iaas/Content/Functions/home.htm). This notebook walks you through the end to end process of creating a model, deploying it to Functions and calling the REST API to compute an inference.
+* [project.ipynb](project.ipynb): [Projects](https://docs.oracle.com/en-us/iaas/data-science/using/manage-projects.htm) are containers used to manage and secure your data science resources. Each project contains a collection of notebooks sessions and models that are associated with a project. This notebook demonstrates the ADS tools to programmatically interface and work with Projects from the notebook session.
+* [vault.ipynb](vault.ipynb): Your data is only as secure as your passwords, key and other secrets. The best security practice is to never store this information in your notebook. Anyone with access to the notebook will be able to access your secrets. By having the secrets stored in a notebook, you could accidentally commit them to git. By using the [OCI Vault service](https://docs.oracle.com/en-us/iaas/Content/KeyManagement/Concepts/keyoverview.htm) you can securely store your secrets and retrieve them at run-time. ADS provides a number of tools that make accessing and storing secrets an easy task. This notebook demonstrates the use of these tools.
+

ads_v_2_2_0/api_keys.ipynb renamed to ads_notebooks/api_keys.ipynb

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+Conda Environment Notebooks
+===========================
+
+
+The [OCI Data Science service](https://www.oracle.com/data-science/cloud-infrastructure-data-science.html) uses [conda environments](https://docs.conda.io/projects/conda/en/latest/index.html) to manage the available libraries that a notebook can use. OCI The Data Science service [provides a number of conda environments](https://docs.oracle.com/en-us/iaas/data-science/using/conda_understand_environments.htm) designed to give you the best in class libraries for common data science tasks. A family can consist of one or more conda environments. Each family of conda environments has notebooks that demonstrate how to perform different data science tasks. This section is organized around these conda environment families and provides the notebooks that you need to get you started quickly.
+
+# Conda Environment Families
+
+* data_exploration_and_manipulation: The [Data Exploration and Manipulation conda environment family](https://docs.oracle.com/en-us/iaas/data-science/using/conda-dem-fam.htm) gives you the tools that you need to perform exploratory data analysis and develop a deeper understanding of the data that you are working with.
+* natural_language_processing: The [Natural Language Processing conda environment family](https://docs.oracle.com/en-us/iaas/data-science/using/conda-dem-fam.htm) provides the libraries to perform cutting edge NLP tasks.
+* onnx: [ONNX](https://onnx.ai/) is a standard format to represent machine learning models. It is also the preferred format for storing models in the OCI Data Science service. The [ONNX conda environment family](https://docs.oracle.com/en-us/iaas/data-science/using/conda-onnx-fam.htm) enables you to work with ONNX files.
+* oracle_database: The [Oracle Database conda environment family](https://docs.oracle.com/en-us/iaas/data-science/using/conda-database-fam.htm) is focused on the tools that are needed it interact with databases in general. However, there is an emphasis on using the Oracle Autonomous Databases.
+* pypgx: [PyPGX](https://docs.oracle.com/cd/F41824_01/latest/) is a graph toolkit based on the [Parallel Graph AnalytiX (PGX](https://docs.oracle.com/cd/E56133_01/latest/index.html) libraries. The [PyPGX conda environment family](https://docs.oracle.com/en-us/iaas/data-science/using/conda-pypgx-fam.htm) provides a graph query language [(PGQL)](https://pgql-lang.org/spec/1.3/), optimized analytics algorithms, and graph machine learning tools.
+* pyspark: The [PySpark conda environment family](https://docs.oracle.com/en-us/iaas/data-science/using/conda-pyspark-fam.htm) allows you to create and run [PySpark](https://spark.apache.org/docs/latest/api/python/) operations within the notebook session. It is also a great create and debug PySpark application before submitting them to the [OCI Data Flow service](https://www.oracle.com/big-data/data-flow/) which is OCI's Spark service.
+* pytorch: [PyTorch](https://pytorch.org/) is a machine learning library that is used in applications such as NLP, computer vision, and much more. The [PyTorch conda environment family](https://docs.oracle.com/en-us/iaas/data-science/using/conda-pytorch-fam.htm) supports CPU and GPU versions.
+* rapids: [RAPIDS](https://developer.nvidia.com/rapids) is a GPU-only library that is designed for data science workflows. The [RAPIDS conda environment family](https://docs.oracle.com/en-us/iaas/data-science/using/conda-rapids-fam.htm) enables you to make the most out of OCIs NVIDIA GPU based computing.
+* tensorflow: [TensorFlow](https://www.tensorflow.org/) is a machine learning platform that is focused on deep neural networks. The [TensorFlow conda environment family](https://docs.oracle.com/en-us/iaas/data-science/using/conda-tensor-fam.htm) has support for CPU and GPU.
+