Master backend development skills for AI/ML applications
RESTful APIs
Design principles, HTTP methods, status codes, endpoints
GraphQL APIs
Schema design, resolvers, queries, mutations
API Security
Authentication, authorization, JWT, OAuth
API Testing
Unit tests, integration tests, API documentation
SQL Databases
Schema design, queries, optimization, migrations
NoSQL Databases
Document stores, key-value stores, graph databases
Vector Databases
Embedding storage, similarity search, indexing
Data Modeling
Database design patterns, relationships, optimization
Containerization
Docker containers, orchestration, scaling
Cloud Services
AWS, GCP, Azure integration and deployment
CI/CD Pipelines
Continuous integration, deployment automation
Monitoring & Logging
Application monitoring, logging, alerting
Caching Strategies
In-memory caching, distributed caching, CDN
Asynchronous Processing
Message queues, task queues, webhooks
Load Balancing
Traffic distribution, high availability, failover
Performance Optimization
Query optimization, profiling, bottleneck analysis
Security Best Practices
OWASP guidelines, secure coding, vulnerability scanning
Encryption & Hashing
Data encryption, password hashing, secure storage
Rate Limiting
API throttling, DDoS protection, request validation
Compliance & Auditing
GDPR, HIPAA, audit logging, data protection
Track your progress through essential backend development skills
Focus on building scalable and secure systems for AI applications