📋 Phase 43 Planning — Automated Machine Learning (AutoML) & Neural Architecture Search

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🤖 Phase 43 — Automated Machine Learning (AutoML) & Neural Architecture Search

Vision

Phase 43 introduces a comprehensive AutoML and Neural Architecture Search (NAS) framework that automates the entire machine learning pipeline — from architecture design to hyperparameter optimization to end-to-end pipeline assembly. Building on our causal reasoning (Phase 40), distributed systems (Phase 42), and meta-learning foundations, this phase creates self-optimizing AI systems that can design, tune, and deploy themselves.

Sub-Phases

#	Component	Description
43.1	SearchSpaceDesigner	Define neural architecture search spaces, operation sets, macro/micro search, cell-based designs
43.2	NASController	DARTS differentiable search, ENAS weight-sharing, ProxylessNAS, one-shot supernets
43.3	HyperparameterTuner	Bayesian optimization, Hyperband, BOHB, TPE, multi-fidelity optimization
43.4	PipelineAssembler	End-to-end ML pipeline construction, automated feature engineering, model selection
43.5	AutoMLOrchestrator	Unified AutoML framework, meta-learning warm-starts, ensemble selection, resource scheduling

Academic Foundations

• Zoph & Le (2017) — Neural Architecture Search with Reinforcement Learning (original NAS)
• Liu et al. (2019) — DARTS: Differentiable Architecture Search (gradient-based NAS)
• Pham et al. (2018) — Efficient Neural Architecture Search via Parameter Sharing (ENAS)
• Cai et al. (2019) — ProxylessNAS: Direct NAS on Target Task and Hardware
• Real et al. (2019) — Regularized Evolution for Image Classifier Architecture Search
• Snoek et al. (2012) — Practical Bayesian Optimization of Machine Learning Algorithms
• Li et al. (2017) — Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
• Falkner et al. (2018) — BOHB: Robust and Efficient Hyperparameter Optimization at Scale
• Bergstra et al. (2011) — Algorithms for Hyper-Parameter Optimization (TPE)
• Feurer et al. (2015) — Efficient and Robust Automated Machine Learning (Auto-sklearn)
• He et al. (2021) — AutoML: A Survey of the State-of-the-Art
• Elsken et al. (2019) — Neural Architecture Search: A Survey

Architecture Overview

┌─────────────────────────────────────────────────────────────┐
│                    AutoMLOrchestrator                        │
│              (meta-learning · ensemble · scheduling)        │
├──────────┬──────────┬──────────────┬────────────────────────┤
│ Search   │  NAS     │ Hyperparameter│  Pipeline             │
│ Space    │Controller│   Tuner       │  Assembler            │
│ Designer │          │               │                       │
├──────────┼──────────┼──────────────┼────────────────────────┤
│ Op sets  │ DARTS    │ Bayesian opt │ Feature engineering    │
│ Cell     │ ENAS     │ Hyperband    │ Model selection        │
│ design   │ Proxyless│ BOHB / TPE   │ Pipeline optimization  │
│ Macro/   │ One-shot │ Multi-fidelity│ Cross-validation      │
│ micro    │ supernet │ Early stop   │ Auto-ensemble          │
└──────────┴──────────┴──────────────┴────────────────────────┘

Integration Points

• Phase 14 (Self-Modifying Code) — Architecture modifications feed into code synthesis
• Phase 16 (Self-Optimization) — Performance profiling guides search
• Phase 36 (Evolutionary) — Regularized evolution for architecture search
• Phase 40 (Causal Reasoning) — Causal analysis of architecture-performance relationships
• Phase 42 (Distributed Systems) — Distributed NAS training and evaluation

Success Metrics

• Architecture search convergence within 100 GPU-hours equivalent
• Hyperparameter tuning outperforms random search by ≥40%
• Pipeline assembly achieves ≥95% of manually-tuned performance
• Meta-learning warm-starts reduce search time by ≥60%
• End-to-end AutoML completes within configurable resource budgets