NEO

What is NEO?

NEO is the first autonomous ML engineer that works inside VS Code to help you build, debug, and iterate on machine learning and LLM systems faster. Powered by 11 specialized agents coordinated by a multi-step reasoning engine, it handles data exploration, model training, fine-tuning, and deployment without leaving your editor.

Top Features:

• Autonomous ML Pipeline: handles data engineering, training, tuning, and deployment automatically.
• Multi-Agent System: coordinates 11 specialized agents for complex ML workflow tasks.
• VS Code Integration: works directly inside your editor with full repository context.

Use Cases:

• ML Engineers: automate repetitive model training, tuning, and experiment tracking workflows.
• Data Scientists: explore datasets, clean data, prototype models, and debug training loops.
• LLM Developers: build RAG systems, fine-tune models like Llama and Qwen efficiently.

Who Can Use NEO?

• ML Engineers: shipping production machine learning systems who want faster iteration cycles.
• Data Science Teams: running experiments across multiple models and datasets in parallel.
• AI Startups: building LLM-powered applications with limited engineering resources available.

Pricing

• Early Access: pricing details available upon request through the official website.
• Enterprise: deployable in your own VPC with Snowflake, Databricks, and BigQuery support.

Pros and Cons

Pros:

• Kaggle Grandmaster Level: scored 34% on MLE-Bench, topping the ML engineering leaderboard.
• Full Pipeline Coverage: handles everything from data exploration to model deployment.
• Native Data Integrations: connects to Snowflake, Databricks, BigQuery, and more natively.

Cons:

• VS Code Only: currently limited to Visual Studio Code as the development environment.
• No Public Pricing: must request access to learn about costs and available plans.
• Early Stage: still in early access phase with limited public documentation available.

FAQs:

1) What makes NEO different from other coding assistants?

it focuses specifically on ML engineering with 11 specialized agents for the full pipeline.

2) Can NEO fine-tune modern language models?

yes, it supports fine-tuning Llama 3, Qwen, Gemma, and other modern model architectures.

3) Does NEO work with cloud data platforms?

yes, it integrates natively with Snowflake, Databricks, and Google BigQuery platforms.

4) How well does NEO perform on benchmarks?

it scored 34.22% on MLE-Bench across 75 Kaggle competitions, topping the leaderboard.

5) Can NEO be deployed on-premise?

yes, enterprise deployment is available in your own VPC for full data control.