#Hyperparameter Tuning

FLAML is a lightweight Python library that excels at AutoML and hyperparameter tuning across various tasks, such as classification and regression. With minimal computational requirements, it offers extensive customization for optimizing machine learning models and next-gen GPT-X applications using automated multi-agent frameworks. The library is perfectly designed to handle complex constraints while integrating seamlessly with MLflow and Microsoft Fabric Data Science for comprehensive MLOps/LMOps solutions.

Explore Kubernetes-native AutoML that seamlessly automates processes like hyperparameter tuning, early stopping, and neural architecture search across multiple frameworks such as TensorFlow, PyTorch, and MXNet. This open-source project integrates efficiently with Kubernetes resources and tools such as Kubeflow Training Operator and Argo Workflows, supporting algorithms including Random Search and Bayesian Optimization. Discover framework compatibility with Goptuna, Hyperopt, and Optuna, and initiate efficient model tuning with the Python SDK.

Discover a versatile array of reinforcement learning agents, designed to showcase adaptable practices across multiple environments. Utilizing Stable Baselines, this toolkit facilitates the fine-tuning of hyperparameters, offering user-friendly interfaces for training, testing, and deploying agents. Perfect for educational exploration and development, it serves both novices and expert developers looking to refine their skills in robust model creation. Opt to transition to RL-Baselines3 Zoo for the latest advancements.

Open Source Vizier is a Python-based service that offers flexible tools for reliable black-box optimization in research and hyperparameter tuning. It features three main APIs for users, developers, and benchmarking. These APIs enable distributed optimization of black-box objectives. Advanced use cases include TensorFlow Probability and PyGlove support. The service is easy to install with options catering to diverse needs, and is compatible with Python 3.10+. Detailed documentation ensures efficient implementation.