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Features

Comprehensive, actionable capabilities for autonomous ML engineering—from local data analysis to production-ready deployment—optimized for VS Code workflows.


Quick Overview

NEO accelerates AI/ML workflows with local-first autonomous agents:

Faster Iterations

Automate repetitive tasks and reduce iteration time by 10x

ML Capabilities

Support for 50+ task types across tabular, vision, NLP, audio, LLMs, and more

Deployment Flexibility

Cloud, local, on-prem, or edge deployment with reproducible artifacts

Reproducibility

Complete versioning of models, datasets, and pipeline outputs


Core Capabilities

End-to-End Pipeline

Automate the full ML lifecycle locally:

Advanced Capabilities

Multi-Step Reasoning

Chooses the optimal strategy by evaluating multiple approaches before execution

Adaptive Intelligence

Automatically adapts strategy based on dataset characteristics and user feedback

Production-Ready Artifacts

Fully documented, tested, and versioned outputs for reliable deployment


ML Pipeline

Data Ingestion → Validation → Feature Engineering → Model Training → Evaluation → Deployment

Pipeline Features

Data Ingestion

Load data from local files, databases, or cloud storage

Validation

Schema checks, anomaly detection, and quality validation

Feature Engineering

Automated creation, transformation, and selection of features

Model Training

Multi-model training with hyperparameter optimization

Evaluation

Metrics, visualizations, and comparative reports

Deployment

One-click deployment to cloud, local, or edge environments


Supported ML Tasks

Tabular ML

Regression, Classification, Clustering, Time Series, Anomaly Detection

Computer Vision

Image Classification, Object Detection, Segmentation, OCR

NLP

Sentiment Analysis, NER, Summarization, Question Answering

Audio & Speech

Speech Recognition, Audio Classification, Speaker Identification

LLM Fine-tuning

Instruction tuning, LoRA/QLoRA, domain adaptation

GenAI & Agentic AI

RAG systems, autonomous agents, tool calling, multi-step reasoning

Advanced & Multi-modal

Recommendations, transfer learning, multi-modal pipelines


Key Advantages

Speed & Efficiency

Quality & Reliability

Flexibility & Control


Deployment Options

Cloud Platforms

AWS, GCP, Azure managed services for scalable deployment

VS Code Extension

Local, private, and Git-friendly development environment

On-Premises

Enterprise-grade security and compliance on local infrastructure

Edge & Mobile

Lightweight deployment for IoT and mobile devices

Output Formats

Docker Containers

Ready-to-run containers with all dependencies included

REST APIs

Fully documented OpenAPI endpoints for integration

Pickle / ONNX

Model serialization for reuse in custom pipelines

Cloud-native

Deploy to SageMaker, Vertex AI, or other managed platforms


Feature Highlights

Autonomous Execution

Self-corrects, installs dependencies, and handles errors automatically

AI-Powered

Full Transparency

View all code, decisions, and reasoning for each action

Explainable

Iterative Improvement

Refines models and workflows based on validation and feedback

Adaptive

Smart Artifacts

Generates reproducible reports, metrics, plots, and models

Production-Ready