ML Practice: Gap Analysis (Consolidated)¶

~3 минуты чтения

Полный анализ пробелов в 93 задачах Приоритизированный список новых задач для добавления Обновлено: 2026-02-11

Executive Summary¶

Current Coverage: 93 tasks across 8 categories Identified Gaps: 40+ topics missing for complete AI/ML/LLM Engineer preparation Priority 1 Gaps: 15 topics (should add ASAP) Priority 2 Gaps: 12 topics (useful for Senior+)

Gap Categories¶

Category A: Missing ML Domains (High Impact)¶

Gap	Frequency	Level	Justification
Computer Vision (ViT, YOLO)	HIGH	Mid/Sr	Only CNN covered
Time Series (ARIMA, Prophet)	MEDIUM	Mid	Zero coverage
Graph Neural Networks	MEDIUM	Sr	Growing demand
Generative Models (VAE, GAN, Diffusion)	HIGH	Mid/Sr	Only mentioned
Reinforcement Learning	MEDIUM	Sr	Zero coverage
NLP Classic (NER, POS, Word2Vec)	LOW	Mid	LLM-focused only

Category B: Missing Production Topics (Critical)¶

Gap	Frequency	Level	Justification
Feature Stores	HIGH	Sr	Essential infrastructure
ML CI/CD	HIGH	Mid/Sr	Production standard
Cost Optimization	MEDIUM	Sr	Business critical
Experiment Tracking	HIGH	Mid	Team collaboration
Data Quality	MEDIUM	Mid	Production reliability

Category C: Missing LLM Topics (High Impact)¶

Gap	Frequency	Level	Justification
Model Architecture (MoE, GQA)	HIGH	Sr	Interview standard
Long Context Handling	HIGH	Sr	2025-2026 trend
Agentic Systems (deep)	HIGH	Sr	Growing demand
Evaluation Frameworks	MEDIUM	Mid	Production need
Efficient Training	MEDIUM	Sr	Cost critical
Multimodal LLMs	MEDIUM	Sr	Future direction

Category D: Missing Math Topics (Foundation)¶

Gap	Frequency	Level	Justification
VC Dimension, PAC Learning	LOW	Sr	Theory depth
Matrix Calculus	MEDIUM	Mid/Sr	Backprop understanding
Sampling Methods (MCMC)	LOW	Sr	Bayesian ML
Scaling Laws	MEDIUM	Sr	LLM understanding

Priority 1: Add These 15 Tasks¶

ML Math (3 tasks)¶

math_042_matrix_calculus
  Focus: Jacobian, Hessian, gradient of matrix operations
  Difficulty: Hard

math_043_pac_learning
  Focus: VC dimension, generalization bounds
  Difficulty: Hard

math_044_scaling_laws
  Focus: Chinchilla scaling, compute-optimal training
  Difficulty: Medium

Deep Learning (3 tasks)¶

dl_012_vision_transformer
  Focus: ViT architecture, patch embedding, positional encoding
  Difficulty: Medium

dl_013_diffusion_basics
  Focus: Forward/reverse process, DDPM
  Difficulty: Hard

dl_014_gan_basics
  Focus: Generator/discriminator, training dynamics
  Difficulty: Medium

LLM Engineering (4 tasks)¶

llm_013_agentic_patterns
  Focus: ReAct deep dive, tool use, planning
  Difficulty: Medium

llm_014_long_context
  Focus: RoPE scaling, KV-cache optimization, Ring Attention
  Difficulty: Hard

llm_015_moe_architecture
  Focus: Mixture of Experts, routing, sparse activation
  Difficulty: Hard

llm_016_llm_evaluation
  Focus: MMLU, LLM-as-judge, Arena evaluation
  Difficulty: Medium

ML System Design (3 tasks)¶

mlsd_009_feature_stores
  Focus: Feast, Tecton, online/offline features
  Difficulty: Medium

mlsd_010_ml_cicd
  Focus: Automated testing, deployment, rollback
  Difficulty: Medium

mlsd_011_cost_optimization
  Focus: GPU utilization, spot instances, right-sizing
  Difficulty: Medium

AI Agents (2 tasks)¶

agents_003_tool_use_safety
  Focus: Permission boundaries, validation, human-in-loop
  Difficulty: Medium

agents_004_agent_memory
  Focus: Short-term, long-term, episodic memory systems
  Difficulty: Medium

Priority 2: Add These 12 Tasks¶

Classical ML (2 tasks)¶

ml_017_multi_label_classification
  Focus: Binary relevance, classifier chains
  Difficulty: Medium

ml_018_interpretability
  Focus: SHAP, LIME, feature importance
  Difficulty: Medium

Data Engineering (2 tasks)¶

de_003_feature_store_basics
  Focus: Feature consistency, versioning
  Difficulty: Medium

de_004_data_quality
  Focus: Great Expectations, validation
  Difficulty: Medium

ML System Design (4 tasks)¶

mlsd_012_online_learning
  Focus: Streaming ML, incremental updates
  Difficulty: Hard

mlsd_013_multi_armed_bandits
  Focus: epsilon-greedy, UCB, Thompson Sampling
  Difficulty: Medium

mlsd_014_causal_inference
  Focus: Propensity score, uplift modeling
  Difficulty: Hard

mlsd_015_vector_databases
  Focus: ANN indexes, Pinecone/Milvus
  Difficulty: Medium

LLM Engineering (2 tasks)¶

llm_017_multimodal
  Focus: Vision-language models, image tokenization
  Difficulty: Hard

llm_018_reasoning_models
  Focus: o1-style reasoning, test-time compute
  Difficulty: Hard

AI Agents (2 tasks)¶

agents_005_human_loop
  Focus: Human feedback, approval workflows
  Difficulty: Medium

agents_006_multi_agent_orchestration
  Focus: Hierarchical, parallel, collaborative patterns
  Difficulty: Hard

Priority 3: Nice to Have (13 tasks)¶

# Time Series
ts_001_arima_basics
ts_002_prophet_basics
ts_003_deepar_basics

# Graph ML
graph_001_gnn_basics
graph_002_knowledge_graphs

# Reinforcement Learning
rl_001_q_learning
rl_002_policy_gradient

# NLP Classic
nlp_001_word2vec
nlp_002_ner_pos

# Advanced Optimization
opt_001_lbfgs
opt_002_natural_gradient

# Bayesian ML
bayes_001_bnn_basics
bayes_002_uncertainty

Gap Impact Matrix¶

Gap Category	Current Coverage	After Priority 1	After Priority 2
ML Math	90%	95%	95%
Classical ML	85%	85%	95%
Deep Learning	80%	95%	95%
LLM Engineering	65%	90%	98%
ML System Design	70%	90%	98%
Data Engineering	75%	75%	95%
MLOps	60%	60%	60%
AI Agents	70%	90%	98%

Implementation Roadmap¶

Phase 1 (Week 1-2): Priority 1 Tasks¶

Create 15 new ContentBlocks
Focus on most impactful gaps
Add cross-references to existing tasks

Phase 2 (Week 3-4): Priority 2 Tasks¶

Create 12 additional ContentBlocks
Fill in production-focused gaps
Add system design case studies

Phase 3 (Ongoing): Priority 3 Tasks¶

Add based on user demand
Track interview frequency changes
Update based on 2025-2026 trends

Gap Detection Sources¶

Interview Reports - 15+ company reports analyzed
Job Postings - Indeed, LinkedIn, levels.fyi
Reddit/Telegram - r/MachineLearning, ODS.ai
Courses - Stanford CS, DeepLearning.AI
Papers - ArXiv 2024-2026

Metrics for Success¶

Metric	Current	Target
Task Count	93	120+
Category Coverage	85%	95%
Senior+ Coverage	60%	85%
Production Coverage	65%	90%

This gap analysis should be reviewed quarterly to track interview trends.