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Welcome to Oracle Database's GHW: AI/ML Week Challenges!

Hello hackers! This week, we're going to learn how to leverage several of Oracle's database technologies through a series of fun and interactive challenges!

Getting Help

  • If you have any questions about Oracle or their Global Hack Week challenges, head to the MLH Discord and find the #ask-oracle channel!
  • Each coding challenge is accompanied by a LiveLab tutorial that will walk you through each challenge step by step
  • If you need additional resources, you can find them at the bottom of this page!

Registration Challenges

Registration Challenge 1

Sign up and download 23ai VirtualBox

Head over to the VirtualBox signup page to get set up with a new account.

Registration Challenge 2

Create an account and Sign in for LiveLabs

Create a LiveLabs account so you can leverage Oracle's LiveLab tutorials

Coding Challenges

Coding Challenge 1

Build a RAG application in 7 easy steps with LangChain and Oracle AI Vector Search

Objectives:

  • Learn how to use the popular open source Python LangChain framework to search your PDF documents with natural language.
  • The application will load a chosen PDF document, chop it up into chunks, vectorize and index those chunks.
  • Build a simple ChatBot interface to allow natural language questions to be asked about data in your PDF documents.
  • This RAG [retrieval augmented generation] architecture is powered by Oracle AI Vector Search, a feature of Oracle Database 23ai.

Documentation:

Coding Challenge 2

An introduction to Oracle AI Vector Search using SQL

Objectives:

  • Learn the fundamentals of vector search and how it can be applied to similarity search, RAG [retrieval augmented generation] and finding outliers.
  • Learn how to create, query and modify vectors using SQL.
  • See how vector search uses a ‘closest match given the available data’ approach.
  • See how that you can combine vector search with relational queries for advanced attribute filtering.

Documentation:

Coding Challenge 3

Get started with Oracle Machine Learning Fundamentals on Oracle Autonomous Database

Objectives:

  • Get a quick tour of Oracle Machine Learning technologies on Autonomous Database.
  • Use OML Notebooks to create and evaluate models and score data using SQL, Python and R.
  • Use OML Services REST API to deploy models and score data. Use AutoML UI for a no-code machine learning experience.

Documentation:

Coding Challenge 4

Introduction to Oracle Machine Learning for Python on Autonomous Database

Objectives:

  • In this hands-on lab, experience Oracle Machine Learning for Python on Oracle Autonomous Database.
  • OML4Py supports scalable in-database data exploration and preparation using native Python syntax, invocation of in-database algorithms for model building and scoring, and embedded execution of user-defined Python functions from Python or REST APIs.
  • OML4Py also includes the AutoML interface for automated algorithms and feature selection, and hyperparameter tuning. Join us for this tour of OML4Py.

Documentation:

Coding Challenge 5

MySQL : Machine Learning for Beginners using HeatWave AutoML

Objectives:

  • Discover how HeatWave’s built-in capabilities enable the development of machine learning models directly within the MySQL database.
    • HeatWave ML simplifies machine learning for both novice users and experienced practitioners.
    • By providing the data, HeatWave ML analyzes its characteristics and creates an optimized machine learning model for generating predictions and explanations.
  • In this challenge, participants will create and use a predictive machine learning model.
  • The process includes preparing data, training a model using the ML_TRAIN routine, and generating predictions and explanations with the ML_PREDICT_ and ML_EXPLAIN_ routines.
  • Finally, participants will assess the model's quality using the ML_SCORE routine and view model explanations to understand the workings of their model.
    • All these routines are executed within the HeatWave MySQL Database.

Documentation:

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