simaticai 2.7.0
AI Software Development Kit
License: MIT
Requires Python: >=3.10.0

<img src="https://github.com/industrial-edge/ai-sdk/raw/main/documentation/assets/images/logo-siemens.png" width="120px" />
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# AI Software Development Kit 
## Siemens AG - Industrial AI Suite 
### Streamline Industrial AI Development and Deployment
 
The AI Software Development Kit (the `simaticai` Python Package) is a comprehensive Python library designed to simplify the creation, packaging, and testing of AI inference pipelines for the `Industrial AI Suite`. Part of Siemens' Industrial Edge ecosystem, this SDK accelerates the integration of AI solutions into manufacturing environments.

### Key Features 
+ **Complete ML Pipeline Support**: Create and package AI inference pipelines with ease
+ **Notebook-Based Tutorials**: Ready-to-use [End to End Tutorials](https://github.com/industrial-edge/ai-sdk-tutorials/) for model training and deployment
+ **Industrial Edge Integration**: Seamless connectivity with SIMATIC and Industrial Edge infrastructure
+ **Cloud Compatibility**: Native integration with leading cloud-based ML environments (such as [Microsoft Azure](https://github.com/industrial-edge/reference-architecture-for-industrial-ai-on-azure)) <br> 
+ **GPU Acceleration**: Optimized for NVIDIA GPU-powered Industrial PCs 
+ **Production-Ready**: Built for industrial-grade reliability and performance
 
### Quick Links
#### Documentation & Resources
+ [Industrial AI Suite Overview](https://www.siemens.com/global/en/products/automation/topic-areas/industrial-ai/industrial-ai-suite.html) <br>
+ [Developer Documentation](https://docs.industrial-operations-x.siemens.cloud/r/en-us/2.4.0/ai-sdk-operation-manual) <br>
+ [AI SDK Tutorials](https://code.siemens.com/siemens-ai-launcher-sail/ai-designer/ai-sdk-tutorials) <br>
+ [Microsoft Azure Reference Architecture](https://github.com/industrial-edge/reference-architecture-for-industrial-ai-on-azure)

#### Support
+ Enterprise-grade Siemens support <br>
+ Industrial Edge ecosystem backing <br>
+ Regular updates and security patches <br>
+ Technical consultation available <br>
+ Website: https://support.industry.siemens.com/

### Quick Start
The code examples only represent the main steps to create an AI Inference Pipeline using Package simaticai, to enjoy the full experience, please study the [public tutorials](https://github.com/industrial-edge/ai-sdk-tutorials/) or discover the [code repository](https://github.com/industrial-edge/ai-sdk) on Github.

#### Create an AI inference pipeline
```python
from simaticai.deployment import PythonComponent, Pipeline
# creating a Pipeline Step for classification
classification = PythonComponent(name="classification")  

# [..] additional steps to add resources and defining the environment

# creating of the Pipeline
pipeline = Pipeline("Image Classification")  
# adding the classification step
pipeline.add_component(classification)  

# [..] final steps to define the Pipeline properties and behavior
```

### Package for deployment
```python
# saving the Pipeline for deployment
package_path = pipeline.export("./deploy")
```

## Prerequisites
+ Python >=3.10
+ pip >= 21.3.1 (automatically upgraded during installation)
+ Compatible with Industrial Edge devices
+ NVIDIA GPU support (recommended)

## Why choose AI SDK? 
🏭 Bridge the gap between AI development and shop floor deployment <br>
🚀 Accelerate time-to-value for industrial AI solutions <br>
🔄 Streamline ML operations across multiple locations <br>
🛠️ User-friendly tools for automation engineers <br>
🔌 Native integration with SIMATIC and Industrial Edge ecosystem <br>
☁️ Cloud-ready architecture

## Part of Industrial AI Suite
This SDK is a core component of the Industrial AI Suite, which provides:
+ Seamless cloud integration <br>
+ Complete MLOps infrastructure <br>
+ Multi-location model scaling <br>
+ Industrial Edge ecosystem integration <br>
+ User-friendly deployment tools <br>
+ Production monitoring capabilities <br>


## Benefits
### For Data Scientists
+ Focus on model development while we handle deployment <br>
+ Familiar notebook-based workflows <br>
+ Seamless integration with existing ML tools <br>
+ Support for most used frameworks and multiple libraries <br>

### For Automation Engineers
+ No prior data science experience required <br>
+ User-friendly deployment interfaces <br>
+ Integrated monitoring solutions <br>

### For Operations
+ Scale AI solutions across locations <br>
+ Reliable industrial-grade performance <br>
+ Fast return on investment <br>

# License
MIT license - Contact Siemens for licensing options