NEO

NEO is the first autonomous ML Engineer designed to save you from thousands of hours of grunt work by automating the entire machine learning workflow. It is powered by a system of agents that work in parallel to solve your most urgent and important ML engineering problems.

Key Features:

• NEO uses multi-step reasoning with its extensive knowledge base and GPU sandbox computing to perform iterative ML experimentation for automatic model optimization, understanding tasks, running hundreds of experiments, and automatically evaluating their performance against targets to select the best models.
• NEO provides an interactive chat interface that allows users to guide its exploration of models and approaches, providing context and expertise to accelerate tasks and projects, thereby streamlining the ML workflow with flexible and responsive assistance.
• NEO's pathfinding abilities enable it to proactively explore multiple approaches, assess potential outcomes, and evaluate risks to find the most effective solution for your challenge, leveraging its reasoning capabilities to anticipate challenges and refine its recommendations for a swift and successful path forward.

Use Cases:

• NEO can assess a proper execution workflow with implementation steps including data engineering, ML engineering, training, and evaluations to come up with a final solution when given a problem statement.
• NEO is capable of building a machine learning model with provided data, handling the entire process from start to finish.
• NEO can perform end-to-end model building and problem-solving, transforming the world of ML engineering by allowing engineers to focus on innovation instead of grunt work.

Benefits:

• NEO significantly reduces the time spent on repetitive and time-consuming tasks, cutting down tasks that take weeks into a few hours.
• NEO enhances the capabilities of ML engineers, making them superhuman by automating model optimization and providing intelligent guidance through its chat interface.
• NEO ensures a more efficient and effective ML workflow by leveraging state-of-the-art models and software, allowing engineers to focus on more innovative and creative aspects of their work.

• Automates machine learning workflows
• Optimizes models through iterative experimentation
• Conducts model selection and hyperparameter tuning
• Trains, deploys, and maintains machine learning models
• Facilitates interactive model exploration via chat interface
• Automates data preprocessing and feature engineering
• Evaluates and monitors model performance
• Resolves IT tickets using AI-driven solutions
• Automates ticket triage and resolution processes
• Manages and automates user management tasks like password resets and access requests
• Streamlines IT service desk operations
• Identifies, monitors, and resolves system problems
• Optimizes supply chain processes such as demand planning