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quickstart-ai-ml-functions

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# ML Functions on Confluent Cloud Quickstart

[![Sign up for Confluent Cloud](https://img.shields.io/badge/Sign%20up%20for%20Confluent%20Cloud-007BFF?style=for-the-badge&logo=apachekafka&logoColor=white)](https://www.confluent.io/get-started/?utm_campaign=tm.pmm_cd.q4fy25-quickstart-ai-ml-functions&utm_source=github&utm_medium=demo)

Build real-time ML pipelines with [Confluent Cloud](https://www.confluent.io/confluent-cloud/). This quickstart provisions core Confluent infrastructure (Kafka, Flink, Schema Registry) and includes hands-on labs using Confluent's built-in Flink ML functions.

<table>
<tr>
<th width="25%">Lab</th>
<th width="75%">Description</th>
</tr>
<tr>
<td><a href="./Lab1-Walkthrough.md">Lab 1 – Predictive Maintenance</a></td>
<td>Real-time CNC machine anomaly detection using per-machine ARIMA models on vibration signals — flags bearing degradation before failure occurs. <img src="./assets/lab1/lab1-architecture.png" alt="Lab 1 architecture diagram"></td>
</tr>
<tr>
<td><a href="./Lab2-Walkthrough.md">Lab 2 – Payment Fraud Detection</a></td>
<td>Real-time fraud detection on a synthetic card-transaction stream using <code>ML_DETECT_ANOMALIES</code> with per-customer ARIMA models to flag spike transactions the moment they exceed each customer's normal spending ceiling. <img src="./assets/lab2/Lab2-architecture.png" alt="Lab 2 architecture diagram"></td>
</tr>
<tr>
<td><a href="./Lab3-Walkthrough.md">Lab 3 – Network Capacity Forecasting</a></td>
<td>Real-time per-tower capacity forecasting using <code>ML_FORECAST</code> with per-tower ARIMA models to alert before network congestion impacts customers. <img src="./assets/lab3/lab3-architecture.png" alt="Lab 3 architecture diagram"></td>
</tr>
</table>

## Prerequisites

**Required accounts & credentials:**

[![Sign up for Confluent Cloud](https://img.shields.io/badge/Sign%20up%20for%20Confluent%20Cloud-007BFF?style=for-the-badge&logo=apachekafka&logoColor=white)](https://www.confluent.io/get-started/?utm_campaign=tm.pmm_cd.q4fy25-quickstart-ai-ml-functions&utm_source=github&utm_medium=demo)

**Required tools:**

- **[Confluent CLI](https://docs.confluent.io/confluent-cli/current/overview.html)**
- **[Git](https://github.com/git/git)**
- **[Terraform](https://github.com/hashicorp/terraform)**
- **[uv](https://github.com/astral-sh/uv)**

<details>
<summary> Installation commands (Mac/Windows)</summary>
**Mac:**

```bash
brew install uv git python && brew tap hashicorp/tap && brew install hashicorp/tap/terraform && brew install --cask confluent-cli
```

**Windows:**

```powershell
winget install astral-sh.uv Git.Git Hashicorp.Terraform ConfluentInc.Confluent-CLI Python.Python
```
</details>

## Quick Start

**1. Clone the repository and navigate to the Quickstart directory:**

```bash
git clone https://github.com/confluentinc/quickstart-ai-ml-functions.git
cd quickstart-ai-ml-functions
```

**2. One command deployment:**

```bash
uv run deploy
```

That's it! The script will guide you through setup and deploy all labs by default. To deploy a single lab: `uv run deploy lab1`

## Directory Structure

```
quickstart-ai-ml-functions/
├── terraform/
│   ├── core/              # Shared Confluent Cloud infra for all labs
│   ├── lab1/              # Lab 1 resources
│   ├── lab2/              # Lab 2 resources
│   ├── lab3/              # Lab 3 resources
│   └── lab4/              # Lab 4 resources
├── scripts/deploy.py      # Start here with uv run deploy
└── scripts/               # Python utilities invoked with uv
```

## Cleanup

```bash
# Automated
uv run destroy
```

## Sign up for early access to Flink AI features

For early access to exciting new Flink AI features, [fill out this form and we'll add you to our early access previews.](https://events.confluent.io/early-access-flink-features)

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