AI Development Hub

AI Development Hub adalah platform untuk pengembangan dan deployment AI models. Dirancang untuk mendukung seluruh siklus hidup pengembangan AI, dari research hingga production.

Layanan Kami

Model Training

Infrastructure untuk training AI models dengan berbagai framework.

Supported Frameworks

• PyTorch: Deep learning framework
• TensorFlow: Machine learning platform
• Scikit-learn: Traditional ML
• Hugging Face: NLP models
• JAX: High-performance ML

Training Infrastructure

Resource	Spesifikasi
GPU	NVIDIA Tesla T4
CPU	8 vCPUs
RAM	32 GB
Storage	500 GB NVMe

Training Features

• Distributed Training: Multi-GPU support
• Hyperparameter Tuning: Automated optimization
• Experiment Tracking: MLflow integration
• Model Versioning: DVC for version control

Inference Engine

Deploy trained models untuk production inference.

Deployment Options

Type	Use Case	Latency
Real-time	API endpoints	< 100ms
Batch	Bulk processing	Minutes-hours
Edge	On-device	Varies

Inference Features

• Auto-scaling: Scale based on demand
• A/B Testing: Model comparison
• Monitoring: Real-time metrics
• Version Control: Model versioning

API Integration

Standardized APIs untuk integrasi dengan ecosystem Patabuga Enterprise.

API Endpoints

# Prediction API
POST /api/v1/predict
Content-Type: application/json
{
  "model": "model-name",
  "input": {...}
}

# Training API
POST /api/v1/train
Content-Type: application/json
{
  "dataset": "dataset-id",
  "config": {...}
}

# Model Management
GET /api/v1/models
POST /api/v1/models/{model-id}/deploy

API Features

• Authentication: API key based
• Rate Limiting: Configurable limits
• Documentation: OpenAPI spec
• SDKs: Python, JavaScript, Go

Data Pipeline

Tools untuk data processing dan management.

Pipeline Components

• Data Ingestion: Import dari berbagai sumber
• Data Cleaning: Automated preprocessing
• Feature Engineering: Feature extraction
• Data Validation: Quality checks
• Data Versioning: DVC integration

Data Formats

• Structured: CSV, JSON, Parquet
• Unstructured: Images, Text, Audio
• Streaming: Real-time data processing
• Batch: Large dataset processing

Use Cases

Natural Language Processing

• Text classification
• Sentiment analysis
• Named entity recognition
• Machine translation
• Question answering

Computer Vision

• Image classification
• Object detection
• Image segmentation
• Face recognition
• Video analysis

Speech Processing

• Speech recognition
• Text to speech
• Speaker identification
• Audio classification

Recommendation Systems

• Collaborative filtering
• Content-based filtering
• Hybrid approaches
• Real-time recommendations

Getting Started

Prerequisites

1. Account: PES Research account
2. Dataset: Data untuk training
3. Model: Model architecture atau pre-trained model

Steps

1. Upload Dataset: Import data ke platform
2. Configure Training: Set hyperparameters
3. Start Training: Jalankan training job
4. Evaluate Model: Assess model performance
5. Deploy Model: Deploy ke inference engine
6. Monitor: Track model performance

Example Workflow

# 1. Upload dataset
from pes_ai import Dataset
dataset = Dataset.upload("my-dataset", "./data/")

# 2. Configure training
from pes_ai import TrainingConfig
config = TrainingConfig(
    model="bert-base-uncased",
    dataset=dataset,
    epochs=10,
    batch_size=32
)

# 3. Start training
from pes_ai import Trainer
trainer = Trainer(config)
model = trainer.train()

# 4. Deploy model
model.deploy(name="my-model", version="1.0")

# 5. Make predictions
from pes_ai import Predictor
predictor = Predictor("my-model")
result = predictor.predict("Hello world")

Pricing

Service	Biaya	Unit
GPU Training	$/hour	Per GPU hour
Inference	$/1000 requests	API calls
Storage	$/GB/month	Data storage
Data Transfer	$/GB	Outbound data

Cost Optimization

1. Use spot instances: Untuk training non-critical
2. Batch processing: Lebih hemat untuk bulk
3. Model compression: Kurangi inference cost
4. Cache results: Hindari redundant computations

Security

Data Protection

• Encryption: Data terenkripsi at rest & in transit
• Access Control: Role-based permissions
• Audit Trail: Semua akses tercatat
• Compliance: GDPR, HIPAA ready

Model Security

• Model Encryption: Model files terenkripsi
• Access Logging: Semua inference tercatat
• Version Control: Model versioning & rollback
• Backup: Automatic model backup

Support

Documentation

• API Reference: Lengkap dengan contoh
• Tutorials: Step-by-step guides
• Best Practices: Recommended patterns
• Troubleshooting: Common issues & solutions

Technical Support

• Email: ai-support@patabuga.co
• Response Time: < 4 jam untuk critical issues
• Dedicated Support: Untuk enterprise customers

Community

• Forum: Diskusi dengan komunitas
• GitHub: Open source tools & examples
• Events: Webinars & workshops

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AI Development Hub - From Research to Production