Kubeflow
Kubeflow makes deployment of ML Workflows on Kubernetes straightforward and automated
The foundation of tools for AI Platforms on Kubernetes. How to get started with Kubeflow Logical projects that make up Kubeflow Information about Kubeflow AI reference platform and distributions About Kubeflow and its community Externally developed projects that integrate with Kubeflow We're sorry this page wasn't helpful. If you have a moment, please share your feedback so we can improve.
Unsloth
Unsloth is an open-source, no-code web UI for training, running and exporting open models in one unified local interface.
Unsloth lets you run and train AI models on your own local hardware. Run and train Google's new Gemma 4 models! A new open, no-code web UI to train and run LLMs. New Qwen3.5 Small Medium LLMs are here! Run the new 4B and 120B models by NVIDIA. Train MoE LLMs 12x faster with less VRAM. Learn to run local LLMs via Claude OpenAI. Run fine-tune the new 80B coding model. Run fine-tune 30B model for agentic coding. Unsloth streamlines local training, inference, data, and deployment Search + download + run any model like GGUFs, LoRA adapters, safetensors. Train and RL 500+ models ~2x faster with ~70% less VRAM (no accuracy loss) Supports full fine-tuning, pre-training, 4-bit, 16-bit and FP8 training. Enables LLMs to predict if a headline impacts a company positively or negatively. Can use historical customer interactions for more accurate and custom responses. Fine-tune LLM on legal texts for contract analysis, case law research, and compliance. You can think of a fine-tuned model as a specialized agent designed to do specific tasks more effectively and efficiently. Fine-tuning can replicate all of RAG's capabilities, but not vice versa.
Kubeflow
Unsloth
Kubeflow
Unsloth
Kubeflow
Unsloth
Kubeflow
Unsloth
