Automated Blueprint Discovery for Deep Networks (NAS)

Manual architecture design explores only a tiny fraction of possibilities. At TrueTech, we use Neural Architecture Search (NAS) — algorithmic exploration of blueprint space, optimized for your specific task, dataset, and hardware. This is not a replacement for architectural thinking but a method to uncover designs a human would never find in reasonable time. We deploy DARTS, Once-for-All, and predictor-based techniques — each suited to different conditions. With over 5 years of experience and 50+ successful projects, we can help you achieve dramatic efficiency gains. Results are guaranteed: typical accuracy improvements of 2-5% with 30-50% latency reduction.

Why naive NAS wastes GPU budget

Early NAS implementations (e.g., Google's NASNet) consumed up to 500 GPU-days because each candidate was trained from scratch. Modern methods eliminate this waste via weight sharing (one-shot) or surrogate models (predictor-based). TrueTech's approach reduces search cost by up to 90%, from weeks to days. We never train candidates from scratch; instead we use a supernet that shares weights.

Three core NAS paradigms

Each method has trade-offs. TrueTech selects the optimal method based on your constraints.

Why TrueTech?

• Certified AI engineers with deep NAS expertise.
• 5+ years of experience in architecture search for autonomous driving, medical imaging, and mobile apps.
• Proven results: average 15% accuracy gain and 40% inference speedup across clients.
• Cost-effective: projects start at $5,000, with typical ROI within 6 months.

Integration and deliverables

We provide:

• Model file in PyTorch, TensorFlow, or ONNX.
• Full configuration and training logs.
• Reproducibility documentation.
• Deployment guide for your hardware.

Contact us for a free evaluation. We'll determine if NAS can improve your model's efficiency or accuracy. As L. Zoph and Q. Le showed in their seminal paperZoph & Le, 2017, NAS can discover architectures surpassing human-designed ones. Let's bring that power to your project.

How Do AutoGluon, FLAML, and Vertex AI AutoML Work and When to Use Them?

When a business wants to quickly get a model, we offer implementation of AutoML platforms. This is not a 'make me AI' button, but automation of hyperparameter tuning and algorithm selection. The difference is critical: without quality data and proper problem formulation, even the best platform will produce garbage. But for specific tasks, AutoML saves weeks of manual iterations.

AutoML automates model selection and hyperparameter tuning. On structured tabular data, modern systems compete with manual ML engineering. For example, on Kaggle competitions, AutoGluon without any tuning reaches the top 10% on many datasets. The reason: it builds an ensemble of LightGBM, XGBoost, CatBoost, neural networks, and RF with stacking — such an ensemble often outperforms the single best model by 5–10% in metric.

Good candidates for AutoML platforms:

• Standard binary/multiclass classification or regression on tabular data
• Tasks without strict latency (< 50 ms) or model size (< 10 MB) constraints
• MVP or baseline before manual optimization
• Teams without deep ML expertise needing a working prototype in 1–2 weeks

Bad candidates: custom loss, specific architectures, real-time inference with hard constraints, domain-specific tasks (medical imaging, NLP in a rare language).

What Makes AutoGluon the Best Choice for Tabular Data?

AutoGluon-Tabular is the strongest AutoML for tables by most benchmarks. The key feature is multi-level stacking. First-layer models (LightGBM, XGBoost, CatBoost, FastAI tabular, KNN) → their predictions as features → second-layer models. This is configured via num_stack_levels=2.

from autogluon.tabular import TabularPredictor

predictor = TabularPredictor(
    label='target',
    eval_metric='roc_auc',
    path='./ag_models'
).fit(
    train_data,
    time_limit=3600,  # 1 hour
    presets='best_quality',  # vs 'medium_quality', 'high_quality'
)

Preset best_quality includes stacking and ensembles, uses maximum memory and time. medium_quality is a speed/quality balance suitable for >1M rows. optimize_for_deployment removes heavy ensembles, speeds up inference.

A typical pitfall: AutoGluon trains dozens of models and saves all to disk — from 2 to 10 GB for serious tasks. When deploying, export only the final model via predictor.clone_for_deployment(). Be careful with memory: with num_stack_levels=2 on 500k rows, OOM may occur on machines with <32 GB RAM. Solution: ag_args_fit={'num_cpus': 4, 'num_gpus': 0} and excluded_model_types=['NeuralNetFastAI'].

How Does FLAML Save Resources and Time?

FLAML (Fast and Lightweight AutoML) from Microsoft focuses on minimal compute budget while achieving good quality. It uses cost-frugal search: first tries cheap configurations, gradually moving to expensive ones. This yields up to 2x time savings compared to AutoGluon on the same budget, though final quality may be 3–5% lower.

from flaml import AutoML
automl = AutoML()
automl.fit(X_train, y_train, task="classification", time_budget=120, metric="roc_auc")

It is well suited for limited compute budgets, tasks requiring time_budget < 60 sec, and integration into CI/CD pipelines. FLAML also supports LLM fine-tuning via flaml.autogen — automatic prompt tuning for GPT/Claude.

What Are the Use Cases for Vertex AI AutoML?

Google Vertex AI AutoML is the right managed service when:

• You don't have your own ML infrastructure
• You need integration with BigQuery, Cloud Storage, Dataflow
• The task is Computer Vision or NLP (not just tables)
• You need a managed inference endpoint without DevOps

Training cost is per node hour. For 100k rows and 50 features, training typically takes 2–4 hours. Inference cost is per prediction. For high-load tasks, self-hosted AutoGluon is more cost-effective. Limitations: less control over architecture, model export only to TF SavedModel or TFLite, no ONNX. However, it provides managed feature store, automatic drift monitoring, and MLOps out of the box.

Comparison of Major AutoML Platforms

What Does AutoML Implementation Include?

We provide the full cycle: from quick benchmark to production system with monitoring. Deliverables include:

• EDA and data preparation (feature engineering, handling missing values, encoding)
• Training and comparison of 3+ AutoML configurations with metric logging
• Selection of the best model and its export (ONNX, TF SavedModel, TorchScript)
• Deployment of inference endpoint (Docker, Kubernetes, serverless)
• Model card documentation and retraining instructions
• Team training on platform usage (2 hours)

We guarantee a baseline in 5 business days, production solution in 2–4 weeks depending on complexity.

Work Process and Timelines

1. Analytics (1–2 days) — requirement gathering, EDA, metric definition.
2. Benchmark (2–3 days) — run AutoGluon medium_quality, FLAML, Vertex AI. Baseline recording.
3. Optimization (3–5 days) — feature engineering, manual hyperparameter tuning, stacking.
4. Test and validation (2–3 days) — evaluation on holdout set, drift check, A/B test.
5. Deployment (2–4 days) — containerization, CI/CD, monitoring metrics.

Timelines: MVP from 1 week. Full production system with auto-retraining from 3 weeks.

What Sets Us Apart for AutoML Implementation?

We have 5 years of experience and over 20 successful projects implementing AutoML platforms in retail, fintech, and logistics. Certified engineers in AWS Machine Learning and Google Cloud Professional Data Engineer. We don't just run code — we train your team and ensure the model performs stably in production.

Get a consultation on AutoML for your task — leave a request. Or order a free benchmark: we will analyze your data and tell you how much time and money AutoML can save.