Tracks
Track 1: Cloud Computing
- Cloud-native architectures and microservices
- Kubernetes and container orchestration
- Serverless computing and function-as-a-service
- Multi-cloud and hybrid cloud strategies
- Infrastructure as Code (IaC) and automation tools
- Cloud migration and application modernization
- DevOps and CI/CD in the cloud
- Observability, monitoring, and AIOps
- Cost optimization and FinOps
- Edge computing and CDN optimization
- Cloud governance and lifecycle management
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- Cloud networking and network architectures
- Disaster recovery and high availability
- Cloud-native databases and distributed storage
- Service mesh and inter-service communication
- GitOps practices and policy as code
- Cloud sustainability and green computing
- Cloud security and compliance frameworks
- HPC and scientific computing in the cloud
- Confidential computing and privacy-preserving clouds
- Cloud innovation for IoT and digital twins
- Quantum computing integration with cloud services
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Track 2: Cloud & AI
- MLOps and model deployment in the cloud
- Cloud ML platforms
- AutoML and low-code ML platforms
- Scalability of AI workloads
- GPU computing and hardware acceleration
- Large-scale distributed training
- Model serving and inference optimization
- Edge AI and IoT deployment
- Cognitive AI and intelligent applications
- Computer vision applications and deployment
- AI cloud ethics (fairness, transparency, accountability in cloud ML ops)
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- LMM and multimodal applications
- Responsible AI and model governance
- Automated ML pipelines
- Cloud-native feature stores
- Real-time ML inference
- Cost optimization for AI workloads
- Hybrid AI
- Big Data in the cloud
- AIOps (AI for Cloud operations)
- AI model compression and deployment efficiency (quantization, pruning)
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Track 3: Data Sciences & Data Engineering
- Data collection and ingestion
- Data storage
- Data processing
- Cognitive AI applications
- Large-scale ETL/ELT pipelines
- Workflow orchestration
- Modern data architecture (Data lake, Data Warehouse, Lakehouse)
- Real-time data processing and streaming
- Metaheuristics and optimization models
- High Dimension Data (HDD) and dimension reduction
- Data quality and validation
- Machine learning and deep learning
- Generative AI and LLMs (RAG, fine-tuning, prompt engineering)
- LMM (Large Multimodal Models) - vision, audio, text
- Computer vision (object detection, segmentation, tracking, OCR)
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- Data analytics and business intelligence
- Advanced analytics
- Feature engineering and feature stores
- Exploratory analysis and visualization
- Time series and forecasting
- Recommender systems
- Data governance and lineage
- Big Data architectures and ecosystems
- Big Data analytics and visualization
- Big Data integration with AI/ML pipelines
- Scalable machine learning on Big Data
- Big Data governance and metadata management
- Responsible Data Engineering (data ethics, fairness, data bias)
- DataOps and MLOps integration
- Cloud-native engineering pipelines
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Track 4: Cybersecurity
- Cloud security
- Zero Trust Architecture
- Identity and Access Management (IAM)
- Container security and Kubernetes security
- DevSecOps and Security as Code
- Threat detection and response
- SIEM and SOC automation
- Vulnerability management
- Cloud compliance
- Encryption and key management
- Network security (firewalls, WAF, DDoS protection)
- API security
- Security monitoring and logging
- Incident response in the cloud
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- Microservices architecture security
- Data protection and privacy by design
- AI/ML for cybersecurity (anomaly detection, threat intelligence)
- Computer vision for security (surveillance, biometrics, anomaly detection)
- Generative AI security (prompt injection, jailbreaking, adversarial attacks)
- LLM and LMM security (data leakage, model extraction)
- Threat intelligence
- Backup, recovery and business continuity
- Post-quantum cryptography
- AI for security automation (AIOps, SOAR)
- Supply chain security
- Red team / Blue team exercises
- Security analytics and threat hunting
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