Hopsworks
Build, deploy, and scale production ML systems with Hopsworks. The Feature Store and MLOps platform for real-time AI, trusted by leading teams.
Founded in 2017, Hopsworks originated from a strong foundation in academia and the open-source community (ex MySQL, Oracle). As a pioneering platform for data and AI, Hopsworks seamlessly integrates the disciplines of data science, data engineering, and machine learning into a cohesive environment. An AI Lakehouse is a modern infrastructure designed to support the unique needs of AI and machine learning workloads. It simplifies the deployment and development of AI models and provides a structured, efficient approach to building and maintaining AI systems, enabling faster model creation and smoother production deployment. Palo Alto 94301, California USA 69 Wilson St EC2A2BB, London United Kingdom Plan 2 116 24, Stockholm Sweden Contact us and learn how Hopsworks can help your organization deploy reliable AI systems.
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.
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