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   "source": [
    "# Install the necessary dependencies\n",
    "\n",
    "import os\n",
    "import sys \n",
    "!{sys.executable} -m pip install --quiet pandas scikit-learn numpy matplotlib jupyterlab_myst ipython"
   ]
  },
  {
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   "source": [
    "---\n",
    "license:\n",
    "    code: MIT\n",
    "    content: CC-BY-4.0\n",
    "github: https://github.com/ocademy-ai/machine-learning\n",
    "venue: By Ocademy\n",
    "open_access: true\n",
    "bibliography:\n",
    "  - https://raw.githubusercontent.com/ocademy-ai/machine-learning/main/open-machine-learning-jupyter-book/references.bib\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d51de614",
   "metadata": {},
   "source": [
    "# Data Science in the cloud\n",
    "\n",
    "![cloud-picture](https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/cloud-picture.jpeg)\n",
    "\n",
    "> Photo by [Jelleke Vanooteghem](https://unsplash.com/@ilumire) from [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape)\n",
    "\n",
    "When it comes to doing data science with big data, the cloud can be a game changer. In the next three sections, we are going to see what the cloud is and why it can be very helpful. We are also going to explore a heart failure dataset and build a model to help assess the probability of someone having heart failure. We will use the power of the cloud to train, deploy and consume a model in two different ways. One way uses only the user interface in a Low code/No code fashion, and the other way uses the Azure Machine Learning Software Developer Kit (Azure ML SDK).\n",
    "\n",
    "![project-schema](https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/project-schema.png)\n",
    "\n",
    "---\n"
   ]
  }
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