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workshop-ai-as-api

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최종 버전 다운로드 (.zip)

- settings.json
- - db.py
  - models.py
- - aiModelTest.py
  - apiRequestTest.py
  - paginationTest.py
- AIModel.py
- config.py
- dbio.py
- main.py
- minimain.py
- MockSpamAIModel.py
- schema.py
- ai_as_api_badge.png
- astra-db-get.png
- astra-setup-token.png
- astra_get_to_cql_console.gif
- astranaut.png
- coding_enterpreneurs.jpg
- create_astra_db_button.png
- dot-env-2.png
- during_training.png
- gitpod_gotoline.png
- gitpod_view.png
- jupyter_on_gitpod_annotated.png
- launch-course.png
- launch-gitpod.png
- miniapi_requests.png
- neural_config.png
- swagger_ui.png
- workshop-cover.png
- prepareDataset.ipynb
- AI-as-API-Python-FastAPI-text-classifier.pdf
- - spam-dataset.csv
- - README
- - README
.app-env.sample
.gitignore
.gitpod.yml
astra.json
loadTestModel.py
prepareDataset.py
README.md
requirements.txt
trainModel.py

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# Workshop: AI as an API (W026)

**The full course, including hands-on instructions, is on [DataStax Academy](https://datastax.academy/course/view.php?id=10). There,
you will learn how to work in the Interactive Lab**.

_For best results: (1) Check the course [start page](https://datastax.academy/course/view.php?id=10)._
_(2) Enroll in the course. (3) Start **learning** and **practicing**!_

<p align="center">
<a href="https://datastax.academy/course/view.php?id=10">
<img src="images/launch-course.png?raw=true" width="200" alt="Start the course on DataStax Academy" />        
</a>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
<a href="https://gitpod.io/#https://github.com/datastaxdevs/workshop-ai-as-api">
<img src="images/launch-gitpod.png?raw=true" width="200" alt="Start the Interactive Lab on Gitpod" />
</a>
</p>

### Learn to build your own NLP text classifier and expose it as an API using the following technologies:

<img src="images/workshop-cover.png?raw=true" width="350" align="right" />

- AI-based text analysis with Tensorflow/Keras;
- Astra DB, a Database-as-a-Service built on Apache Cassandra®;
- FastAPI, the high-performance Python framework for creating APIs;
- Many useful Python libraries and packages including `pandas`, `pydantic`, `dotenv`, `sklearn`, `uvicorn`, and more.

### During this hands-on workshop, you will:

- prepare the labeled dataset for model training;
- train the model to classify any input text;
- export the trained model and test it interactively;
- create your free NoSQL database for data storage;
- set up and start an API exposing the classifier as a reusable class;
- learn how to speed up the API with DB-based caching;
- inspect how a streaming response is handled in the API.

#### Prerequisites:

- Familiarity with Python;
- A Github account is required for the hands-on.