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Move AI from the cloud to the edge. Google AI Edge Develop low-latency, privacy-oriented and high-performance artificial intelligence applications that can run on Android, iOS and Web platforms.
2026's greatest technology Edge AI Step into the world. your models Eka Sunucu GPU Train it on the infrastructure and distribute it to devices with MediaPipe and TensorFlow Lite. Push the limits with hybrid cloud architecture.
Edge AI, artificial intelligence algorithms are processed on the device where the data is created (smartphones, IoT devices, browsers) rather than on a central cloud server. Google AI Edge is at the heart of this transformation.
Traditional cloud-based AI models require constant internet connection and create data privacy concerns. However, with Edge AI technology, data is processed without ever leaving the device. This is KVKK and GDPR This is a critical advantage for compatibility.
Gemini Nano, Google's most efficient artificial intelligence model, is designed to work directly on Android devices. It is managed through the AICore system and performs operations such as text summarization, smart replies and language translation without an internet connection.
Advanced structure that can process not only text but also image and audio data on the device.
It provides maximum performance with minimum battery consumption by using Neural Processing Unit (NPU) hardware.
Possibility to customize the model with your own custom data sets with Low-Rank Adaptation (LoRA).
Google AI Edge ecosystem for developers MediaPipe Tasks and TensorFlow Lite It offers ready-made solutions. These tools make integrating complex ML models into mobile and web applications a breeze.
However, you need a strong backend infrastructure to train and optimize these models. here it is Server with Graphics Card (GPU) Our services come into play. Train your models on our powerful servers, convert them to .tflite format and distribute them.
Below you can see an object detection code example for a Python-based rendering server or Edge device using MediaPipe. This code is from Eka Sunucu. VDS Servers It runs smoothly in the Python environment.
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import mediapipe as mp
from mediapipe.tasks import python
from mediapipe.tasks.python import vision
import cv2
# Model yolunu ve konfigürasyonu belirle
model_path = 'efficientdet_lite0.tflite'
BaseOptions = mp.tasks.BaseOptions
ObjectDetector = mp.tasks.vision.ObjectDetector
ObjectDetectorOptions = mp.tasks.vision.ObjectDetectorOptions
VisionRunningMode = mp.tasks.vision.RunningMode
# Opsiyonları ayarla: Resim modu, max sonuç sayısı ve eşik değeri
options = ObjectDetectorOptions(
base_options=BaseOptions(model_asset_path=model_path),
max_results=5,
score_threshold=0.5,
running_mode=VisionRunningMode.IMAGE
)
# Eka Sunucu VDS üzerinde bir görseli işleme
def detect_objects_on_server(image_file):
with ObjectDetector.create_from_options(options) as detector:
# Görseli yükle
mp_image = mp.Image.create_from_file(image_file)
# Tespiti realleştir
detection_result = detector.detect(mp_image)
# Resultları işle
for detection in detection_result.detections:
category = detection.categories[0]
print(f"Nesne: {category.category_name} - Güven: {category.score:.2f}")
# Fonksiyonu çalıştır
detect_objects_on_server('product_gorseli.jpg')
This block of code OpenCV and MediaPipe It detects objects on the server side or on a powerful Edge device using its libraries. To manage such operations on the backend side Our SSD Virtual Servers You can choose.
Google AI Edge uses hardware accelerators to improve the performance of models. on Android NNAPI, on iOS Core ML and on the web side WebGPU technologies distribute the processing load from the CPU to special processors.
However, much more powerful hardware is needed to train these models. As Eka Sunucu, we provide high-performance servers to train your artificial intelligence models. NVIDIA GPU We offer well-equipped servers.
The best AI experience is when Edge and Cloud work together Hybrid Architecture It is possible with . While simple operations are performed on the device (Edge), complex analyzes and big data processing processes are performed in Eka Sunucu's secure cloud infrastructure (Cloud).
High-performance servers optimized for model training, backend API services and data storage.
For Python scripts and lightweight model deployment.
Ideal for TensorFlow and PyTorch training.
For large-scale LLM and GenAI projects.
Frequently asked questions about Edge AI and Google's artificial intelligence tools.
Because Edge AI processes data on the local device, it eliminates the delay in sending data to the server, does not require an internet connection, and ensures privacy by preventing personal data from leaving the device. However, cloud servers (Cloud AI) are still needed for training large models.
Yes, thanks to MediaPipe Web and TensorFlow.js technologies, you can run browser-based artificial intelligence models on the client-side. Eka Sunucu is used for hosting and fast loading of these models. Speed Hosting We recommend packages.
Gemini Nano runs on modern Android devices with AICore support and sufficient NPU (Neural Processing Unit) power. Devices such as the Google Pixel 8 series and the Samsung S24 series are the first examples to natively support this technology.
Your trained models (.tflite, .onnx) can be served as static files. However, for model versioning, syncing user data, and heavy processing, a GPU-enabled VDS Server Renting will be the most professional approach.