If you are AI/ML practitioner or someone who is starting their AI/ML journey but don’t want to spend hours setting up the right environment , this VM is for you. It includes : - Jupyter – Your AI/ML Playground - Jupyterhub – Making your AI/ML projects more collaborative by providing multi-user environment and enabling easy code and data sharing - Jupyter AI extension(https://jupyter-ai.readthedocs.io/en/latest/index.html) - your gateway to generative AI within Jupyter - Preinstalled popular AI/ML libraries such as TensorFlow, PyTorch, scikit-learn and many more - Pre-configured NVIDIA GPU drivers & CUDA libraries The preinstalled Juputer and AI/ML libraries jump-start your AI/ML development by saving you hours of installation time. Jupyterhub gives you the collaboration capabilities by allowing a multi-user environment within the same VM. This not only makes it easy to share the AI/ML work , but makes it more cost efficient in a team setup by allowing multiple users/team members to share the same VM infrastructure instead of each user creating their own VM/notebooks. With the Jupyter AI(https://jupyter-ai.readthedocs.io/en/latest/index.html) extension, you can seamlessly integrate with 100+ widely used LLMs from 10+ model providers such as OpenAI for ChatGPT, Anthropic, Hugging Face, AI21, SageMaker to name a few. Complete list of supported LLM Model providers is available here.(https://jupyter-ai.readthedocs.io/en/latest/users/index.html#model-providers) The Jupyter-AI extension comes with built-in LLM Chat UI for seamless collaboration for generative AI. Enjoy flexibility with support for diverse models and providers, seek code suggestions, debugging tips, or even have code snippets generated for you by interacting with the chat UI. In addition to the Chat UI, the Jupyter-ai extension comes with %ai and %%ai magic commands turning your Jupyter into a generative AI playground anywhere the IPython kernel runs! The VM also has pre-configured NVIDIA GPU drivers & CUDA libraries saving you hours of driver setup and configuration hassle so you can harness the power of GPU resources for your AI/ML workload and conduct advanced data analysis with ease. More details about JupyterHub and enabling Multiuser environment is here(http://www.techlatest.net/support/jupyter_python_notebook_support/jupyterhub)