1. Scalability: AutoGluon focuses on automating machine learning tasks with the goal of scalability by leveraging the power of deep learning techniques, whereas DeepSpeed is specifically designed to optimize large-scale deep learning model training on distributed systems, emphasizing scalability through efficient parallelism and communication techniques.

2. Automation vs optimization: AutoGluon operates by automating the machine learning pipeline, including hyperparameter tuning and model selection, with a focus on ease of use and minimal user input, whereas DeepSpeed concentrates on optimizing the training process by enhancing the performance and efficiency of deep learning models through techniques like model parallelism and gradient compression.

3. Framework support: AutoGluon is compatible with popular deep learning frameworks such as PyTorch and TensorFlow, providing a higher level of abstraction for users to train models without extensive knowledge of the underlying frameworks, while DeepSpeed is a specialized library specifically tailored for PyTorch, offering advanced features to accelerate training performance in PyTorch-based workflows.

4. Resource utilization: AutoGluon aims to simplify machine learning workflows by automating resource management and model training, abstracting away the complexities of system optimizations, whereas DeepSpeed provides low-level optimizations and distributed training strategies to maximize resource utilization and training speed for large-scale deep learning models by leveraging techniques like gradient checkpointing and mixed precision training.

5. Model support: AutoGluon offers a wide range of built-in models and algorithms for various machine learning tasks, providing a diverse set of pre-configured models to choose from and enabling quick experimentation, while DeepSpeed focuses on enhancing the training of deep learning models, particularly large-scale models like language models and image classifiers, by providing specialized optimizations and features tailored for such models.

6. Community and support: AutoGluon is supported by a community of machine learning practitioners and developers, offering resources such as tutorials, documentation, and forums for users to seek help and collaborate on projects, whereas DeepSpeed is backed by Microsoft Research and is actively maintained with contributions from researchers and engineers, ensuring continued development and support for deep learning projects that require advanced optimizations and performance improvements.

In Summary, AutoGluon emphasizes automation and scalability for machine learning tasks across various frameworks, while DeepSpeed focuses on optimizing and accelerating training of large-scale deep learning models in PyTorch environments.