Learning & Development
Structured learning paths from fundamentals to expert-level knowledge. Each path provides a clear progression through the topics that matter most in modern technology.
In-Demand Skills
Relative demand across the technology landscape (2026)
Learning Paths
Structured progressions from fundamentals to production expertise
AI & Machine Learning Fundamentals
- 01 How neural networks learn (backpropagation, gradient descent)
- 02 Transformer architecture from first principles
- 03 Attention mechanisms and why they replaced RNNs
- 04 Tokenization, embeddings, and vector spaces
- 05 Fine-tuning vs prompt engineering vs RAG
- 06 Building your first RAG pipeline
Database Engineering
- 01 SQL fundamentals and query optimization
- 02 PostgreSQL internals: MVCC, WAL, query planner
- 03 Indexing strategies (B-tree, GIN, GiST, HNSW)
- 04 Vector search with pgvector for AI applications
- 05 Replication, backup, and high availability
- 06 When to use NoSQL, NewSQL, or specialized stores
Cloud-Native Infrastructure
- 01 Linux fundamentals: processes, networking, filesystems
- 02 Containers from scratch (namespaces, cgroups, overlay FS)
- 03 Kubernetes architecture and core objects
- 04 Helm charts, operators, and GitOps with ArgoCD
- 05 Observability: Prometheus, Grafana, OpenTelemetry
- 06 eBPF for networking and security
AI Systems Architecture
- 01 RAG architecture patterns and trade-offs
- 02 Vector databases: indexing, recall, and latency
- 03 Model serving: vLLM, TensorRT-LLM, batching strategies
- 04 Agent architectures: tool use, planning, memory
- 05 Multi-agent orchestration and coordination
- 06 Evaluation frameworks: measuring LLM quality
AI in Financial Services
- 01 Quantitative finance and algorithmic trading basics
- 02 ML models for credit risk and fraud detection
- 03 NLP for earnings calls, filings, and sentiment
- 04 AI governance and model risk management (SR 11-7)
- 05 Regulatory technology and compliance automation
- 06 AI investment landscape and circular financing risks
- 01 Git fundamentals: init, add, commit, status, log, diff
- 02 Branching, merging, and resolving conflicts
- 03 GitHub collaboration: PRs, issues, code review, forks
- 04 Branching strategies: GitHub Flow vs Git Flow
- 05 Advanced Git: rebase, stash, cherry-pick, bisect, reflog
- 06 Configuration, .gitignore, commit conventions, SSH setup
- 01 Define: goal specification, scope, boundaries, failure modes
- 02 Design: tools, prompts, memory strategy, orchestration
- 03 Build: frameworks, structured output, error handling, logging
- 04 Evaluate: test datasets, safety testing, metrics, human review
- 05 Deploy: staged rollout, guardrails, circuit breakers
- 06 Monitor: tracing, drift detection, incident response, cost tracking
- 07 Iterate: continuous improvement from production data
Curated Resources
Books, courses, and hands-on platforms recommended for each discipline
Courses & MOOCs
Stanford CS229 — Machine Learning
Andrew Ng's foundational ML course covering supervised, unsupervised, and reinforcement learning
Fast.ai — Practical Deep Learning
Top-down approach to deep learning, building working models from week one
MIT 6.S191 — Introduction to Deep Learning
Compact, modern deep learning course with hands-on TensorFlow labs
CMU 11-711 — Advanced NLP
Transformer architectures, attention, pre-training, and modern NLP techniques
Books & References
Designing Data-Intensive Applications
Martin Kleppmann's definitive guide to distributed systems and database architecture
Designing Machine Learning Systems
Chip Huyen's practical guide to production ML system design and MLOps
The Art of PostgreSQL
Deep dive into advanced SQL and PostgreSQL features for application developers
Site Reliability Engineering
Google's approach to building and operating large-scale, reliable systems
Hands-On Practice
Kaggle Competitions
Real-world ML challenges with datasets, notebooks, and community solutions
LeetCode — Database Problems
SQL and database design problems for interview prep and skill building
Kubernetes the Hard Way
Kelsey Hightower's guide to bootstrapping Kubernetes from scratch
Build Your Own X
Tutorials for building databases, compilers, web servers from scratch
Skill Maturity Model
Typical progression timeline for each technology area