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Extra ✦

At this stage, we will explore advanced topics in the field of backend development and DevOps that will help you deepen your knowledge and skills in creating reliable and scalable systems for AI Agents. These concepts are especially useful for those aspiring to system architect or tech lead roles in AI projects.

Ask AI Instructions
instruction

Since these topics do not change over time, it is best for you to study them with a personal tutor - ChatGPT.

The learning process should be as follows:

  • you create a system prompt for ChatGPT (templates), where you describe your background, preferences, level of detail of explanations, etc.
  • copy the topic from the list (triple click), and ask ChatGPT to explain this topic to you
  • if you want to delve deeper, ask clarifying questions

At the moment, this is the most convenient way to learn the basics. In addition to concepts, you can study additional materials in the Gold, Silver, Extra sections.

  1. Gold - be sure to study before communicating with ChatGPT
  2. Ask AI - ask questions on each unfamiliar topic
  3. Silver - secondary materials
  4. Extra - in-depth topics

Golden​

10 Sysdes Patterns
Why Kubernetes is so popular
Sysdes
More Sysdes
Ansible
Terraform

Ask AI​

Ask ChatGPT

DevOps and Infrastructure​

  1. Nginx for AI systems: load balancing and request proxying
  2. Kubernetes: orchestrating ML workflows in production (practical cases)
  3. Kubernetes Operators: automating repetitive tasks (Overview)
  4. GitOps for beginners: basic principles and ArgoCD setup
  5. Kubernetes monitoring: Prometheus + Grafana (templates for AI)
  6. Service Mesh: basic concepts of Istio/Linkerd (Briefly)
  7. Helm: application templating (workshop for AI developer)
  8. Canary Deployments: safe model updates (step-by-step guide)
  9. Infrastructure as Code: comparison of Terraform and Pulumi (concept)
  10. CI/CD pipelines: automating model training (end-to-end example)

Highload systems​

  1. DB Sharding: basic strategies for beginners
  2. CQRS + Event Sourcing: architectural patterns (Overview)
  3. Message queues: Kafka vs RabbitMQ (comparison for AI)
  4. Backpressure: protecting systems from overload (practical examples)
  5. Data consistency: basic patterns of distributed systems
  6. Latency optimization: diagnosing problems in AI inference
  7. Caching: multi-level strategies (practical cases)
  8. Observability: monitoring AI pipelines (OpenTelemetry)
  9. Big Data processing: Spark for beginners (basic concepts)
  10. Rate Limiting: API protection (ready-made solutions and libraries)

Security and reliability​

  1. OAuth 2.0: practical implementation for AI systems
  2. Model protection: basic methods against prompt injection
  3. Zero Trust: basic principles (Brief overview)
  4. Secrets Management: working with HashiCorp Vault (guide)
  5. Fault Tolerance: templates for beginners (Overview)
  6. gRPC: optimizing communication between microservices
  7. Blue-Green Deployments: basic scenario for AI models
  8. SLA/SLO/SLI: quality metrics (practical examples)
  9. Security audit: main stages (checklist)
  10. Redundancy: strategies for AI inference (Briefly)

Cloud technologies and financial optimization​

  1. Multi-cloud strategies: reducing dependence on providers for AI systems
  2. FinOps: optimizing costs for cloud GPUs and TPUs for AI projects
  3. Spot Instances: effective use for model training
  4. Serverless for AI: architectural patterns and antipatterns
  5. Cloud Native AI: effective use of cloud ML/AI services
  6. Data Lake and Data Warehouse: architectures for AI data
  7. Edge Computing: moving AI inference closer to data sources
  8. Benchmarking cloud providers: methodology for AI workflows
  9. Pay-as-you-go vs Reserved Instances: strategies for AI startups
  10. Cloud automation: robots for monitoring and optimizing costs

Databases and storage for AI​

  1. Vector DBs: optimizing queries and indexing for RAG systems
  2. Time Series DB: storing and analyzing time series for AI monitoring
  3. NewSQL: modern distributed DBs with ACID guarantees
  4. Data Lakehouse: architecture for AI startups (Delta Lake, Iceberg)
  5. Column Store vs Row Store: choice for analytical AI systems
  6. Embedded DB: local solutions for Edge AI (SQLite, DuckDB)
  7. Transactional Outbox: reliable event transfer between services
  8. Full-text search: Elasticsearch for hybrid search with AI
  9. Database Federation: combining heterogeneous data sources
  10. Graph DB: using for LLM knowledge graphs and recommendations

Silver​

  1. DevOps Roadmap for AI Engineer
  2. Modern cloud application architecture patterns
  3. Ansible vs Puppet vs Chef: comparative analysis
  4. Testing distributed systems: approaches and tools

Extra​

  1. Developing custom Kubernetes operators for AI workflows
  2. EventMesh: global event bus for microservice AI systems
  3. WebAssembly as a runtime environment for lightweight AI models
  4. eBPF: kernel-level monitoring and debugging for high-load AI systems
  5. unikernels: minimalistic specialized OSs for AI inference
  6. Functional programming in backend development: benefits for AI systems
  7. SRE for AI systems: Google practices and processes
  8. Quantum computing for AI: current state and prospects
  9. Zero-downtime database migrations: strategies for continuous operation
  10. Data Sovereignty: compliance with regional requirements for AI data