Weco AI

What is Weco AI?

Weco AI is an advanced machine learning optimization platform that automates ML experiments using AIDE ML technology. This innovative system employs large language model-powered agents to systematically optimize machine learning pipelines through evaluation-driven experimentation.

How Does Weco AI Work?

The platform operates through a sophisticated three-step process:

1. Local Evaluation System

Weco AI runs your code locally on your own infrastructure, ensuring data privacy while maintaining full control over your ML environment. The system connects to your evaluation scripts through a simple command-line interface.

2. Automated Experimentation

Using AIDE ML agents, Weco systematically tests hundreds of code variations, including:

• Architecture modifications (model structure changes)
• Hyperparameter optimization (learning rates, batch sizes)
• Data augmentation techniques (CutMix, RandAugment)
• Performance optimizations (mixed precision, CUDA kernels)
• Training methodology improvements (scheduler changes, regularization techniques)

3. Metric-Driven Optimization

The system continuously evaluates performance against your specified metrics (accuracy, AUC, throughput, etc.) and evolves solutions based on empirical results, creating a tree search of successful variations.

Core Features and Capabilities

🚀 Automated ML Engineering

• Feature engineering automation: Systematically explores and implements feature transformations
• Architecture search: Tests various model architectures and configurations
• Hyperparameter optimization: Explores optimal parameter combinations automatically

⚡ GPU Kernel Optimization

• CUDA/Triton kernel generation: Transforms PyTorch functions into optimized GPU kernels
• Hardware performance maximization: Achieves peak hardware utilization
• Mixed precision implementation: Automatically implements FP16/FP32 mixed training

🤖 Prompt Engineering Automation

• LLM optimization: Automatically experiments with prompt variations
• Systematic testing: Evaluates hundreds of prompt combinations
• Performance tracking: Measures and compares LLM output quality

Practical Applications and Use Cases

Weco AI excels in multiple ML scenarios:

Research and Development

• Academic research: Accelerates ML research by automating experimentation
• Industry R&D: Speeds up product development cycles
• Benchmark optimization: Improves performance on standardized benchmarks

Production ML Systems

• Model performance improvement: Increases accuracy and efficiency of production models
• Infrastructure optimization: Reduces computational costs through better resource utilization
• Deployment readiness: Ensures models are optimized for production environments

Specialized Optimization Tasks

• Computer vision models: Optimizes CNNs, transformers, and other vision architectures
• NLP systems: Improves language model performance and efficiency
• Reinforcement learning: Optimizes RL algorithms and environments

Technical Implementation

The platform supports multiple programming languages and frameworks:

• Primary language: Python (PyTorch, TensorFlow, JAX)
• Additional support: C++, Rust, JavaScript
• Framework compatibility: Works with major ML frameworks and custom implementations
• Hardware flexibility: Supports various GPU architectures (NVIDIA, AMD, Apple Silicon)

Performance and Results

Weco AI has demonstrated significant improvements across various benchmarks:

• CIFAR-10 validation: Achieved +7% accuracy improvement over baseline
• ResNet-18 optimization: 2.3× speedup through mixed precision and DALI implementation
• OpenAI MLE-Bench: 4× more medals than next best autonomous agent
• METR RE-Bench: Outperformed human experts in 6-hour optimization challenges

Who is Weco AI For?

Target Audience

• ML Engineers: Professionals looking to automate and optimize their workflows
• AI Researchers: Academics and researchers seeking to accelerate experimentation
• Data Scientists: Practitioners wanting to improve model performance efficiently
• Tech Companies: Organizations aiming to scale their ML operations

Skill Requirements

• Intermediate ML knowledge: Understanding of machine learning concepts
• Programming proficiency: Comfort with Python and ML frameworks
• Experimental mindset: Willingness to embrace automated experimentation

Getting Started with Weco AI

The platform offers a straightforward onboarding process:

1. Installation: pip install weco
2. Configuration: Point to your evaluation script
3. Execution: Run optimization commands
4. Monitoring: Watch real-time progress through the dashboard

Average onboarding time is under 10 minutes, making it accessible for teams of all sizes.

Why Choose Weco AI?

Competitive Advantages

• Privacy-first approach: Your data never leaves your infrastructure
• Cost efficiency: Achieves more with fewer computational resources
• Systematic methodology: Based on proven AIDE ML research
• Proven results: Demonstrated success across multiple benchmarks
• Open-source foundation: Core technology is open for inspection and contribution

Comparison with Alternatives

Unlike one-shot code generation tools, Weco AI employs systematic evaluation and iteration, ensuring measurable improvements rather than speculative changes.

Pricing and Accessibility

Weco AI uses a credit-based pricing system:

• Free tier: 20 credits (approximately 100 optimization steps)
• No credit card required for initial usage
• Transparent pricing: Clear cost structure based on optimization steps

The platform represents excellent value for ML teams looking to accelerate their research and development cycles while maintaining control over their data and infrastructure.