AutoML-Agent provides a versatile Python-based framework that orchestrates every stage of the machine learning lifecycle through an intelligent agent interface. Starting with automated data ingestion, it performs exploratory analysis, missing value handling, and feature engineering using configurable pipelines. Next, it conducts model architecture search and hyperparameter optimization powered by large language models to suggest optimal configurations. The agent then runs experiments in parallel, tracking metrics and visualizations to compare performance. Once the best model is identified, AutoML-Agent streamlines deployment by generating Docker containers or cloud-native artifacts compatible with common MLOps platforms. Users can further customize workflows via plugin modules and monitor model drift over time, ensuring robust, efficient, and reproducible AI solutions in production environments.

Predibase offers the fastest and most efficient way to fine-tune and serve any open-source large language model. Built specifically for developers, it allows seamless deployment and operation of open-source LLMs on a robust serverless infrastructure. With Predibase, you can manage the entire lifecycle of machine learning models from training to deployment, ensuring high performance and scalability.