[DRAFT] Personal Learning Journey

This post i will try to create my own learning journey to deepen the knowledge related with development

Learning Hierarchy (Dev-Ops)

1. Kernel Level

As an SRE, understanding the kernel is crucial for debugging performance issues and understanding system behavior. Key areas to focus on include:

• Process Management: Understanding how processes are created, scheduled, and managed by the kernel.
• Memory Management: How the kernel allocates and manages memory, including virtual memory.
• File Systems: How the kernel interacts with file systems.
• System Calls: The interface between user-space applications and the kernel.

2. OS & Network

For OS knowledge, SREs should have a solid understanding of:

• Operating System Concepts: Processes, threads, memory management, and file systems.
• System Administration: User management, package management, and basic troubleshooting.
• Security: User authentication, authorization, and common security vulnerabilities.

Regarding networking, familiarity with these terms is essential:

• TCP/IP: The foundation of internet communication.
• HTTP/HTTPS: Protocols for web communication.
• DNS: Domain Name System, for resolving domain names to IP addresses.
• Load Balancing: Distributing network traffic across multiple servers.
• Firewalls: Network security systems that control incoming and outgoing traffic.
• VPN: Virtual Private Network, for secure remote access.

3. CS Fundamentals

• Data Structures and Algorithms:
  • Arrays, linked lists, stacks, queues, hash tables
  • Trees (binary trees, search trees, heaps)
  • Graphs (representation, traversal)
  • Sorting algorithms (merge sort, quicksort, heapsort)
  • Searching algorithms (binary search)
  • Algorithm analysis (Big O notation)
• Operating Systems: Deep understanding of OS concepts like processes, threads, memory management, file systems, and system calls.
• Networking: Solid grasp of networking principles, protocols (TCP/IP, HTTP, DNS), and network architectures.
• Databases:
  • Relational databases (SQL): data modeling, schema design, SQL queries, transactions, indexing
  • NoSQL databases: key-value stores, document databases, column-family databases, graph databases
  • Database administration: backup/restore, performance tuning
• Distributed Systems:
  • Understanding the challenges of distributed systems: consistency, fault tolerance, latency
  • Scalability patterns: sharding, replication, caching
  • Message queues and distributed streaming platforms (Kafka, RabbitMQ)

4. System Admin

• Linux system administration: user management, package management, file system management, process monitoring
• Scripting: Bash, Python (for automation and tooling)
• Configuration management: Ansible, Chef, Puppet (for infrastructure as code)
• Automation: cron, systemd timers
• Cloud platforms: AWS, Azure, GCP (basic administration and services)

5. System Perfomance Checking

• Monitoring tools: Prometheus, Grafana, Datadog, New Relic
• Logging: ELK stack (Elasticsearch, Logstash, Kibana), Fluentd
• Tracing: Jaeger, Zipkin, OpenTelemetry
• Profiling: CPU profiling, memory profiling (using tools like perf, pprof)
• Load testing: JMeter, k6, Locust

Learning Hierarcy (ML/DA Learning)

Learning Material: https://www.deeplearning.ai/

1. Data Mining

• Data cleaning and preprocessing
• Feature extraction and selection
• Association rule mining
• Clustering algorithms
• Data visualization

2. Machine learning

• Supervised learning (regression, classification)
• Unsupervised learning (clustering, dimensionality reduction)
• Model evaluation and selection
• Regularization techniques
• Common algorithms (linear regression, logistic regression, decision trees, support vector machines, k-nearest neighbors)

3. Natural Language Processing

• Text preprocessing (tokenization, stemming, lemmatization)
• Feature extraction (TF-IDF, word embeddings)
• Text classification and sentiment analysis
• Language modeling
• Sequence-to-sequence models

4. Neural Network

• Basic neural network architecture
• Activation functions
• Loss functions and optimization algorithms
• Convolutional neural networks (CNNs)
• Recurrent neural networks (RNNs)

5. Large Language Model

• Transformer architecture
• Pre-training and fine-tuning techniques
• Prompt engineering
• Applications of LLMs (text generation, question answering, summarization)

6. Transformer (Deep Learning Architecture)

• Self-attention mechanism
• Encoder-decoder structure
• Multi-head attention
• Applications in NLP and other domains