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Nikunjbansal99 · Feb 13, 2022 · 04f0f5f · 04f0f5f
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+# O'Reilly Book
+
+## Data Science on AWS
+
+YouTube Videos, Meetups, Book, and Code:  **https://datascienceonaws.com**
+
+[![Data Science on AWS](img/data-science-on-aws-book.png)](https://datascienceonaws.com)
+
+# Workshop Description
+In this hands-on workshop, we will build an end-to-end AI/ML pipeline for natural language processing with Amazon SageMaker.  We will train and tune a text classifier to classify text-based product reviews using the state-of-the-art [BERT](https://arxiv.org/abs/1810.04805) model for language representation.
+
+To build our BERT-based NLP model, we use the [Amazon Customer Reviews Dataset](https://s3.amazonaws.com/amazon-reviews-pds/readme.html) which contains 150+ million customer reviews from Amazon.com for the 20 year period between 1995 and 2015.  In particular, we train a classifier to predict the `star_rating` (1 is bad, 5 is good) from the `review_body` (free-form review text).
+
+# Learning Objectives
+Attendees will learn how to do the following:
+* Ingest data into S3 using Amazon Athena and the Parquet data format
+* Visualize data with pandas, matplotlib on SageMaker notebooks
+* Run data bias analysis with SageMaker Clarify
+* Perform feature engineering on a raw dataset using Scikit-Learn and SageMaker Processing Jobs
+* Store and share features using SageMaker Feature Store
+* Train and evaluate a custom BERT model using TensorFlow, Keras, and SageMaker Training Jobs
+* Evaluate the model using SageMaker Processing Jobs
+* Track model artifacts using Amazon SageMaker ML Lineage Tracking
+* Run model bias and explainability analysis with SageMaker Clarify
+* Register and version models using SageMaker Model Registry
+* Deploy a model to a REST Inference Endpoint using SageMaker Endpoints
+* Automate ML workflow steps by building end-to-end model pipelines using SageMaker Pipelines
+
+# Workshop Agenda
+![Workshop Agenda](img/outline.png)
+
+# Workshop Paths
+
+## Quick Start (All-In-One Workshop Path)
+![Workshop Paths](img/workshop_paths1.png)
+
+## Additional Workshop Paths per Persona
+![Workshop Paths](img/workshop_paths2.png)
+
+# Workshop Contributors
+![Workshop Contributors](img/primary-contributors.png)
+
+# Workshop Instructions
+
+## 1. Login to AWS Console
+
+![IAM](img/aws_console.png)
+
+## 2. Create `TeamRole` IAM Role
+
+![IAM](img/alt_iam_1.png)
+
+![Roles](img/alt_roles_2.png)
+
+![Create Role](img/alt_create_role_3.png)
+
+![Select Service](img/alt_select_service_4.png)
+
+![Select Policy](img/alt_select_policy_5.png)
+
+![Add Tags](img/alt_add_tags_6.png)
+
+![Review Name](img/alt_review_name_7.png)
+
+## 3. Update IAM Role Policy
+
+![Select IAM](img/studio_select_iam.png)
+
+![Edit TeamRole](img/studio_edit_teamrole.png)
+
+Click `Attach Policies`.
+
+![IAM Policy](img/view_policies.png)
+
+Select `AmazonS3FullAccess` and click on `Attach Policy`.
+
+_Note:  Reminder that you should allow access only to the resources that you need._ 
+
+![Attach Admin Policy](img/alt_attach_policies.png)
+
+## 4. Launch SageMaker Studio
+
+Open the [AWS Management Console](https://console.aws.amazon.com/console/home)
+
+![Back to SageMaker](img/alt_back_to_sagemaker_8.png)
+
+In the AWS Console search bar, type `SageMaker` and select `Amazon SageMaker` to open the service console.
+
+![Notebook Instances](img/stu_notebook_instances_9.png)
+
+![Create Studio](img/studio_create.png)
+
+![Pending Studio](img/studio_pending.png)
+
+![Open Studio](img/studio_open.png)
+
+![Loading Studio](img/studio_loading.png)
+
+## 5. Launch a new Terminal within Studio
+
+Click `File` > `New` > `Terminal` to launch a terminal in your Jupyter instance.
+
+![Terminal Studio](img/studio_terminal.png)
+
+## 6. Clone this GitHub Repo in the Terminal
+
+Within the Terminal, run the following:
+
+```
+cd ~ && git clone https://github.com/data-science-on-aws/workshop
+```
+
+If you see an error like the following, just re-run the command again until it works:
+```
+fatal: Unable to create '/home/sagemaker-user/workshop/.git/index.lock': File exists.
+
+Another git process seems to be running in this repository, e.g.
+an editor opened by 'git commit'. Please make sure all processes
+are terminated then try again. If it still fails, a git process
+may have crashed in this repository earlier:
+remove the file manually to continue.
+```
+_Note:  This is not a fatal error ^^ above ^^.  Just re-run the command again until it works._
+
+## 7. Start the Workshop!
+
+Navigate to `00_quickstart/` or `01_oreilly_book/` in your Jupyter notebook and start the workshop!
+
+_You may need to refresh your browser if you don't see the new `workshop/` directory._
+
+![Start Workshop](img/studio_start_workshop.png)